# EarShots Full Documentation & Domain Knowledge # EarShots > EarShots is a developer social listening and AI search visibility product. It finds public conversations about a developer product, sorts them into five tracks, checks public claims against the product's documentation, tracks how AI assistants answer buyer prompts, and drafts documentation-grounded replies on X, LinkedIn and Reddit for human approval. EarShots is built for technical go-to-market teams: developer relations, developer marketing, support, product, and sales. It listens across GitHub, X, LinkedIn, Reddit, Hacker News, Dev.to, Hashnode, blogs, and the wider public developer web, and reads the agent networks where coding agents decide what to install. Every mention is sorted into questions, friction, buying signals, praise, or muted. Muted items carry a reason and can be moved into any track in one tap. Posts by the company's own team, executive interviews, and neutral mentions are kept out of customer tracks, because they are not customer feedback. Ears, the EarShots AI agent, prepares reply drafts from documentation the customer connects. Drafts include citations. Drafting is limited to replies on X, LinkedIn and Reddit; for other posts EarShots offers "Open original post" and "Create ticket" instead. EarShots never posts, comments, or sends messages on a customer's behalf. A person reviews every draft and sends it from the original post. Beyond the queue, EarShots includes: - AEO / GEO Prompt Universe: tracks the prompts buyers ask ChatGPT, Gemini, Google AI Overviews and Claude, and records whether the product is mentioned or cited, which competitors are mentioned, which domains are cited instead, and share of voice across branded and unbranded prompts. Search Sync can be run on demand. - Ground Truth Review: one place to review findings, check the facts in public articles against connected documentation, and inspect individual claims with their source and evidence. A claim without documentation backing is marked "Not enough to judge", never "Accurate". High and medium findings can get an independent web check. - Knowledge Hub: the reviewed facts, guardrails, and reference material that ground drafts and fact checks. Proposed facts need human confirmation before they are used. - Context Buzz: public GitHub usage patterns, blockers, and coding agent activity, in one filterable evidence list. - Competitor Signal: evidence-backed reactions to named rivals, on every paid plan. - Launch Planner: a watch window of up to 30 days on a named release. - Stats: volume, channel mix, tracks, and team response over time. - CLI and MCP: the same workspace from a terminal or an AI client, with drafts only. - Teams: shared workspaces with assignments, notes, and a single workspace admin. ## Product - [Full documentation context](/llms-full.txt): Complete product surfaces, industry definitions, and glossary entries in one full markdown payload. - [EarShots homepage](/): Product overview and an interactive example of the listening, classification, drafting, and approval workflow. - [Pricing](/pricing): Starter, Growth, and Enterprise plan details, feature availability, and common billing questions. - [Enterprise](/enterprise): Social listening and AI search visibility across product workspaces and teams. Includes reviewed replies and campaign materials, Listen Enrich contact context for CRM and outbound workflows, saved-contact exports through the app, MCP, CLI and API, plan capacity and rollout requirements. - [Developer social listening](/developer-social-listening): An explanation of developer social listening, the signals it covers, and how it differs from broad social monitoring. - [AEO / GEO platform](/aeo-prompt-universe): The AEO and GEO platform for dev tool companies, B2B SaaS and AI startups. Tracks whether ChatGPT, Claude, Gemini and Google AI Overviews name and cite a product for the prompts buyers and coding agents ask, with use cases per segment. - [Ears Cite](/aeo-citations): Managed citation outreach. EarShots finds the authors of articles AI engines already cite for your prompts and asks them to include your product where it fits. You never write or review an email. ## Listen Enrich - [Listen Enrich](/product/listen-enrich): Professional context from exact LinkedIn personal profiles for sales teams, with separate verified work-email lookup when eligible. Credit packs: 500 credits for $100, 2,500 for $400; profile 1 credit, verified work email 5 credits; no-match lookups free. - [Listen Enrich guide](/docs/listen-enrich): App and MCP estimate, explicit confirmation and status flow. Repository CLI contacts/enrich commands; npm release pending. REST GET /api/v1/contacts requires contacts:read and reads saved contacts only, without paid work. ## Product surfaces - [Product overview](/product/agentic-social-listening): Every EarShots surface on one page. - [Agentic Social Listening](/product/agentic-social-listening): The single sorted feed. Public posts from GitHub, X, LinkedIn, Reddit, Hacker News and the developer web, plus agent decision trails, filed into questions, friction, buying signals, praise, or muted with a reason, with a 180 day lookback. - [AEO / GEO platform and Prompt Universe](/aeo-geo-platform): Tracked AI search prompts with mention rate, citation rate, competitors mentioned, cited domains, excerpts showing where the brand appears, and share of voice. 100 prompts on Starter, 250 on Growth, and credit-based prompts on Enterprise sized with the EarShots team. - [Ground Truth Review](/product/accuracy-maxxing): Findings, fact checks, and claim cards for public articles, checked against connected documentation, with a correction note a person sends. - [Knowledge Hub](/product/knowledge-hub): Reviewed product facts, guardrails, and reference material, with provenance, that ground every draft and fact check. - [Context Buzz](/product/context-buzz): Public GitHub usage patterns, blockers, and coding agent signal in one evidence list with category filters. Reply drafting is disabled for repositories the workspace does not own. - [Competitor Signal](/product/competitor-signal): 3 rivals on Starter, 5 on Growth, 15 on Enterprise, read over a 120 day window, with sentiment, praise, complaints and launch versus production use. - [Stats](/product/stats): Go to market rollup in two tabs, your own performance and competitor insights, over 7, 30 and 90 day windows. - [Agent Signal](/product/agent-signal): What coding agents decide about your product. Which package they install, where your documentation fails them, what they fix, and which agent reviewed which. - [Launch Planner](/product/launch-planner): Track a named release over 7, 14, or 30 days and read developer reaction while the launch is live. Growth and Enterprise plans. - [Ears Cite](/aeo-citations): Managed outreach to the authors of articles AI engines cite, closing the gap Prompt Universe measures. - [CLI and MCP](/product/cli-mcp): The queue from a terminal with @earshotsdev/cli or from an agent session over MCP. Tools read data and store reply drafts; a person sends them. ## Industries - [Industries overview](/industries): The company types where public developer conversation is the main source of truth about a product. - [Developer tools and APIs](/industries/developer-tools): For teams selling tools and application programming interfaces (APIs) to developers. Review integration questions, public repository usage and competing tools with the source behind each finding. - [AI and language-model infrastructure](/industries/ai-infrastructure): For model providers, inference platforms and AI tooling teams. Review what builders are trying to run, the limits they encounter and the alternatives they consider. - [Open source companies](/industries/open-source): For maintainers and companies with a hosted or commercial offering. Read public implementation questions alongside requests for managed operations, team controls and support. - [Cloud and DevOps platforms](/industries/cloud-devops): For hosting, deployment and infrastructure teams. Review migration discussions with the workload, operating costs and deployment constraints that give them meaning. - [Security and DevSecOps](/industries/security): For application security and developer security teams. Review public integration friction and claims about your coverage against the documentation and evidence available. - [Data and analytics platforms](/industries/data-platforms): For data integration, orchestration and analytics teams. Review public reports of connector limits, schema changes and workload trade-offs with their original context. - [Platform engineering](/industries/platform-engineering): For internal developer portal and platform-tool vendors. Review public discussions about service ownership, paved paths and the work required to keep a platform useful. - [Frontend and design systems](/industries/frontend-design-systems): For component libraries, build tools and design-system vendors. Review implementation questions about accessibility, framework compatibility and customization. - [Edge, IoT and hardware](/industries/edge-iot-hardware): For device software development kit (SDK), firmware and connectivity teams. Review public hardware integration reports without losing the board revision, firmware or operating conditions. - [Blockchain infrastructure](/industries/web3-infrastructure): For node, wallet, indexing and blockchain development-tool providers. Review integration friction and provider comparisons in the context of a specific network and application. - [Fintech and payments APIs](/industries/fintech-payments): For payment, ledger and financial integration platforms. Review public developer questions about webhooks, retries and test environments with the documentation behind a possible answer. - [Ecommerce and storefront platforms](/industries/ecommerce-platforms): For commerce, checkout and storefront platform teams. Review what merchants, agencies and integrators need from a migration before deciding which claims or examples to improve. - [Game engines and realtime infrastructure](/industries/gaming-realtime): For engine, multiplayer, voice and game-backend vendors. Review studio integration questions with their engine version, target platforms and multiplayer requirements. - [Video game studios and publishers](/industries/video-games): For studios, publishers and gaming platforms. Review public player feedback and discovery questions by title, platform and release, so community, product and marketing teams can choose what to address. - [B2B software platforms](/industries/b2b-saas-platforms): For business software with integrations and extensible workflows. Review what administrators and integrators need when they compare, configure or replace a platform. - [Identity and authentication](/industries/identity-auth): For authentication, single sign-on (SSO) and identity platform teams. Review public integration questions with the tenant configuration, framework and account requirements attached. - [Communications APIs](/industries/communications-apis): For email, messaging, voice and video platform teams. Review public integration reports with the channel, region and callback behavior that make them actionable. - [Customer support platforms](/industries/customer-support-cx): For help desk, support inbox and customer experience (CX) platform teams. Review how administrators and developers describe routing, integrations and the tools they are replacing. - [Marketing and growth automation](/industries/martech-automation): For marketing automation, analytics and experimentation platforms. Review integration friction and attribution questions with the event definitions and workflow in view. - [Developer marketing and DevRel agencies](/industries/devrel-agencies): For developer relations (DevRel), technical content and developer marketing agencies. Organize listening around each client's product, audience and decision, with separate workspace context. ## Documentation - [Documentation overview](/docs): Product concepts, safety rules, and links to setup and workflow guides. - [Quickstart](/docs/quickstart): Create a workspace, add listening sources and documentation, run a sync, and review the first results. - [Connect sources](/docs/sources): Configure GitHub, X, LinkedIn, and open web sources, choose useful search terms, and understand refresh behavior. - [Connect documentation](/docs/documentation): Add product documentation used to ground factual reply drafts and citations. - [Understand sorting](/docs/sorting): Learn how Ears classifies mentions as questions, friction, buying signals, praise, or muted. - [Draft and approve replies](/docs/replies): Review source content, documentation citations, draft history, edits, and human approval. - [Team collaboration](/docs/team): Assign mentions, leave notes, hand work to teammates, and review decisions together. - [Prompt Universe](/docs/prompt-universe): Add tracked prompts, run Search Sync, and read mention rate, citation rate, cited domains, and share of voice. - [Ground Truth Review](/docs/accuracy): Check claims in tutorials, comparisons, newsletters, and articles against connected documentation. - [Context Buzz](/docs/context-buzz): Group GitHub activity by product usage patterns and developer blockers while preserving source context. - [Agent Signal](/docs/agent-signal): Sources, the six agent decisions, how attribution works, and the question of whether EarShots ever posts on an agent network. - [Competitor Signal](/docs/competitors): Track 3 rivals on Starter, 5 on Growth, or 15 on Enterprise and review likes, dislikes, themes, adoption stages, and GTM implications. - [Stats](/docs/stats): Report listening volume by day, channel, and track, team response, and competitor coverage, over a window of up to 180 days. - [Launch Reactions](/docs/launch-reactions): Record a release with a watch window of up to 30 days and read how developers reacted, sorted into praise, complaint, demand, and question. Growth and Enterprise plans. - [Agents](/docs/skills): How AI agents work with an EarShots workspace and what they are allowed to do. - [Skills](/docs/skills): Writing and review skills agents can use with EarShots. - [Plans and limits](/docs/plans): Compare source, workspace, team, usage, and competitor allowances across plans. ## Agent access - [Workspace API reference](/docs/api): Preview REST API for stored Mentions, Sentiment rollups, Ground Truth findings and claims, verified Brain nodes, AI Search rollups, competitors and source evidence, Context Buzz, reply drafts, campaigns and campaign stages. Keys are workspace-bound, scoped, expiring and revocable, created by owners/admins in Settings > API access. - [OpenAPI 3.1](/openapi.json): Machine-readable operation, filter, response and error reference. Canonical entry /api/v1 redirects with 307 to /api/public/v1 on the same origin. Preserve Authorization: Bearer credentials through redirects. Never put keys in query strings. REST collection reads default to 25 records, accept at most 100 per page and use inclusive UTC windows of at most 180 days. Follow meta.next_cursor with unchanged date filters. Writes to product content, generation, approvals and posting are not implemented, regardless of reserved permission scopes. Signed outbound webhooks support mention.updated, campaign.stage.updated and ground-truth.finding.created, with 10 active subscriptions per workspace, five queued delivery attempts and exact HTTPS Zapier/Make receiver hosts only. Subscriptions stop if their creating key expires, is revoked or loses manager access. Public API production access and receiver delivery remain unverified until publication. Updated October 22, 2026. - [MCP guide](/docs/mcp): Connect an MCP-compatible AI client such as Claude to an EarShots workspace using OAuth. - [CLI guide](/docs/cli): Install and use the EarShots command-line client for workspace, mention, insight, source, and draft workflows. MCP and CLI access follow the same workspace permissions as the web product. Agent-created replies remain drafts. Saving a draft never publishes it. ## Blog - [EarShots blog](/blog): Product notes and practical writing about developer conversations, product context, and careful responses. - [Introducing EarShots.Dev](/blog/social-listening-aeo-platform): Agentic developer social listening and AI visibility in one platform for technical GTM teams. ## Company EarShots was founded by David Zeledon, CEO and Founder. Dave spent 15+ years in marketing and growth for developer platforms. He led social, search, and product-led growth across v0.app at Vercel, including the biggest month in company history. At Google Labs he was a Developer Relations Engineer leading growth for Jules and a project lead on the Situationally Aware Agents experiment. He ran growth for AthenaHQ.ai, a leading AEO and GEO platform, and for HockeyStack as interim CMO. He has worked in AI search since 2019. EarShots exists because developers discuss stacks in public, and go to market teams need one place that reads that conversation and shows how AI assistants judge their product. - [About the team](/about): David Zeledon, founder and CEO of EarShots, and the story behind the product. ## Key product facts - Supported listening surfaces include GitHub issues and discussions, X, LinkedIn, Reddit, Hacker News, Dev.to, Hashnode, blogs, the wider public developer web, and the agent networks where coding agents make decisions. - Every mention is classified as a question, friction, a buying signal, praise, or muted with a reason. - Factual reply drafts are grounded in up to ten customer-provided sources, including documentation URLs and uploaded markdown files, and include citations. - Casual acknowledgements can be drafted without documentation when no factual product claim is needed. - Reply drafting covers replies on X, LinkedIn and Reddit only. Other posts get Open original post and Create ticket. - Posts from the company's own team, executive interviews, and neutral mentions are kept out of customer tracks. - Ground Truth Review checks article claims against connected documentation. Claims without documentation backing read Not enough to judge. - Prompt Universe tracks 100 prompts on Starter, 250 on Growth, and credit-based prompts on Enterprise sized with the EarShots team, across ChatGPT, Gemini, Google AI Overviews and Claude. - Context Buzz summarizes how developers use a product in GitHub projects and where they get blocked. - Competitor Signal is on every paid plan: 3 rivals on Starter, 5 on Growth, 15 on Enterprise. Agent Signal is on Growth and Enterprise. - Launch Reactions is limited to Growth and Enterprise. A watch window defaults to 7 days and can run up to 30. - Human approval is mandatory. EarShots does not auto-post or contact authors. - Plans are billed monthly. Starter begins at $249 per month and Growth at $400 per month. Enterprise pricing is discussed with the EarShots team. ## Resources - [Resources](/resources): Searchable library of whitepapers, published blog posts, eighteen AI Search News Hub guides, developer listening, the glossary, documentation, and the product video. Filter by format and topic. Every card has its own thumbnail. Articles keep their original URLs. The site menu links straight to this page. - [Developer marketing in the age of AI, Fall 2026](/resources/developer-marketing-in-the-age-of-ai): A sourced report on the AI trust gap among developers, community bans on generated content, AI results for marketing teams, the shift of discovery into docs and AI answers, the channels that still work, response time, and a five step playbook. Figures cited to Stack Overflow, Draft.dev, HubSpot, Jasper, GlobalSpec, TREW Marketing, Similarweb, DevTune, daily.dev, Val Town, and Adobe. ## AI search research for agents Agents can use the [EarShots CLI](/docs/cli) or [Model Context Protocol server](/docs/mcp) to read an authorized workspace. After signing in, inspect `earshots tools --json`. Read stored prompts with `earshots call aeo_get_prompt_universe '{"limit":10}' --json`, then read a returned prompt's answers with `earshots answers PROMPT_ID --days 30 --json`. Replace PROMPT_ID with the returned prompt_id. These read commands do not trigger new engine runs or publish content. The [AI search prompt strategy guide](/ai-search/ai-search-prompt-strategy) explains prompt selection, page evidence, fan-out and measurement. Its product images use fictional demo data. Preserve exact prompts, engine names, capture dates and sources in downstream analysis; product illustrations are not observed results. ## AI Search News Hub - [AI Search News Hub](/ai-search): Source-linked guides to AEO, AI citations, Google AI Search, AI crawlers, browser agents and developer listening. Every claim links to a numbered source with a verification date. - [Answer engine optimization and developer social listening](/ai-search/aeo-and-social-listening-guide): Connect answer engine optimization with developer social listening. Compare citation research, review crawler access and measure product visibility accurately. - [How people use ChatGPT in 2026: implications for product discovery and AEO/GEO](/ai-search/chatgpt-usage-2026): OpenAI Signals shows a rising share of product-information research. See what the findings mean for AEO/GEO content, product fit and accurate AI answers. - [How to choose prompts for an AI search strategy](/ai-search/ai-search-prompt-strategy): Choose AI search prompts around customer decisions, map questions to supporting pages, and measure retrieval, citations and recommendations separately. - [Sources cited in AI answers about developer tools](/ai-search/ai-citation-sources-developer-tools): Compare AI search citation studies without confusing total citation share with top-source share. Build a source audit for your developer-tool category. - [Why does AI recommend your competitor? A citation gap audit for SaaS teams](/ai-search/ai-citation-gap-audit): Diagnose why competitors appear in AI answers. Audit eligibility, product facts, cited sources and repeat results with a practical SaaS citation-gap worksheet. - [Where should SaaS teams invest in third-party AI citations?](/ai-search/third-party-ai-citation-strategy): Choose third-party AI citation opportunities from real buyer questions. Use a practical evidence brief for comparisons, reviews and developer communities. - [AI search attribution in GA4: connect referrals to outcomes](/ai-search/ai-search-attribution-ga4): Build a GA4 report for AI Assistant sessions, landing pages and registrations. Includes setup steps, collection checks and a worked attribution report. - [Which AI-search content should SaaS teams create first?](/ai-search/ai-search-content-priorities): Prioritize AI search content around customer decisions. Choose between references, comparisons, tutorials, research and tools with a practical SaaS workflow. - [Google AI Overviews and AI Mode: a site owner’s guide](/ai-search/google-ai-overviews-ai-mode): Learn how Google AI Overviews and AI Mode select supporting links, what makes a page eligible, and which site improvements Google recommends. - [Does Google require special AEO or GEO optimization?](/ai-search/google-aeo-geo): Google requires no special AEO or GEO setup. Check indexing, improve technical answers, and add relevant schema without mistaking it for a citation guarantee. - [How to measure Google AI Search visibility in Search Console](/ai-search/google-ai-search-search-console-report): A practical guide to Google Search Console's Generative AI performance report: its metrics, dimensions, limits, and a repeatable way to use the data. - [Query fan-out, explained for developer questions](/ai-search/query-fan-out): Google can expand one question into related searches. Learn what query fan-out means, how citation studies measure it and where the evidence is limited. - [ChatGPT Search crawler access: a publisher’s guide](/ai-search/chatgpt-search-crawler-access): Understand OpenAI’s published crawler roles and check the robots.txt, CDN, firewall, and analytics settings that affect ChatGPT Search access. - [Claude web crawlers: a site owner’s guide](/ai-search/claude-crawler-access): Learn what ClaudeBot, Claude-SearchBot, and Claude-User do, how Anthropic documents robots.txt controls, and how to check crawler access. - [AI crawler access and referral traffic for documentation](/ai-search/ai-crawler-crawl-to-referral): Measure provider-level crawl requests against referred page requests. Compare reporting methods and review crawler access rules for your documentation. - [How to make a website usable by browser agents](/ai-search/agent-ready-website): Build agent-usable forms with semantic HTML, explicit error states and stable controls. Test task completion separately from Lighthouse audits and WebMCP. - [Where developers talk about your product in 2026](/ai-search/where-developers-talk): Plan developer monitoring across GitHub, Reddit and technical forums. Compare channel data without assuming activity counts prove an audience migration. - [Replying to developers: community rules and disclosure](/ai-search/reply-to-developers): Review developer-community rules on generated content, promotion and affiliation disclosure. Use a technical reply checklist tailored to each platform. ## Optional - [Product demo video](/demo-video): A 73 second walkthrough of listening, classification, the AEO and GEO prompt universe, Ground Truth drafting, Competitor Signal, MCP, CLI, and human approval. The page embeds the video, describes each section, and links to the docs. The raw file also lives at /earshots-demo.mp4. ## Workspace REST API reference Contract: https://earshots.dev/openapi.json. Human reference: https://earshots.dev/docs/api. Preview release. All product operations below are stored-data GET reads, scoped to the key's workspace. No generation, approvals or posting. Lists: limit 1 to 100 (default 25), cursor, since/until inclusive UTC dates, maximum 180 days. Unknown parameters are rejected. API keys belong in the Authorization Bearer header only. Production verification pending. - GET /api/v1/contacts: contacts:read. Saved professional contacts with exact profile URLs and separately verified work email when available. Dates filter created_at. No enrichment or credit spending. - GET /api/v1/mentions: mentions:read. Nonarchived, deduplicated source posts. Body excerpts are capped at 12,000 characters. - GET /api/v1/sentiment: sentiment:read. Daily listening totals from stored rollups. sentiment_sum / scored gives the average when scored is greater than zero. - GET /api/v1/ground-truth: ground-truth:read. Stored findings with verdict, severity and claim references. Historical review findings may appear. Findings are not automatic fact verification. - GET /api/v1/ground-truth-claims: ground-truth:read. Observed claims with literal evidence spans and source URLs. Observation does not establish accuracy. - GET /api/v1/brain: brain:read. Current verified Knowledge Hub nodes only. Unconfirmed candidates are excluded. - GET /api/v1/aeo: aeo:read. Daily stored AI Search answer, capture, mention and citation counts. No search or generation is triggered. - GET /api/v1/competitors: competitors:read. Tracked competitor names, products and stored summaries. No provider diagnostics. - GET /api/v1/competitor-signals: competitors:read. Stored competitor source evidence and sentiment, linked by competitor_id. - GET /api/v1/context-buzz: context-buzz:read. Nonarchived GitHub evidence with usage, blocker and agent-authorship categories. - GET /api/v1/replies: replies:read. Saved reply draft text and review state. Reading or saving content does not publish it. - GET /api/v1/campaigns: campaigns:read. Stored campaign activation and progress associated with individual prompt themes. - GET /api/v1/campaign-stages: campaigns:read. Stored campaign assets, citations and approval timestamps. No generation or approval operation. --- ## Expanded Product Surface Guides ### Agentic Social Listening (/product/agentic-social-listening) **Hear Everyone Talking About Your Product and Turn Every Mention into Action** Agentic Social Listening is the central queue inside EarShots. It tracks every public mention of your product across GitHub, X, LinkedIn, Reddit, Hacker News, package registries, and the wider web. Each post is automatically sorted as a question, friction point, buying signal, praise, or muted, paired with a drafted response whenever one makes sense. From this single dashboard, technical marketing, product, and sales teams can jump straight into the conversation. *Summary:* Agentic Social Listening is the main screen in EarShots, labeled Insights in the app. Left, the five tracks with counts and the line that decided each one. Right, the selected mention with its draft, the documentation passage it cites, and one button: copy the reply and open the original thread. #### Each post lands where someone can act on it. - **The five tracks**: Counts for questions, friction, buying signals, praise, and muted, plus the line that decided each classification. - **The selected mention**: The post, the drafted reply, the documentation passage it cites, and one button: copy and open the thread. #### From a public post to a reply you can send. EarShots reads the post, decides its track, and shows the line that decided it. For questions and friction it searches your sources, writes a reply, and cites the passage. Muted items carry a reason: wrong entity, directory, round-up, status page, job post, or off-topic, and unmute in one tap. #### Frequently Asked Questions - **Q: What are the five tracks?** A: Questions, friction, buying signals, praise, and muted. Every mention gets one track and a line explaining why, before anyone opens it. - **Q: Can I change a classification?** A: Yes. Move a mention to a different track by hand, and the correction is recorded and used to sort the next one. - **Q: What happens to muted items?** A: They stay in the queue with a reason: wrong entity, directory, round-up, status page, job post, or off-topic. Nothing is deleted, and any item unmutes in one tap. - **Q: Where do drafts come from?** A: Up to ten documentation sources you connect. Every draft cites the passage it used, so a person can check it before sending. ### Context Buzz (/product/context-buzz) **Every repo, issue, and pull request that names your product.** Context Buzz reads public GitHub activity that references your product: repositories, issues, pull requests, and discussions. It shows what developers are building on top of you, which integrations they combine you with, and the errors and blockers that recur, for product and developer relations teams. *Summary:* Left, what they are doing: new repos, integrations, and projects built on top of you, with a humans and agents toggle. Right, what is blocking them: grouped error strings, failing docs URLs, and open questions, each linked to the source. #### Usage and blockers, side by side. - **What they are doing**: New repos, integrations, and projects built on top of your product, with a toggle to separate humans from agents. - **What is blocking them**: Grouped error strings, failing docs URLs, and open questions, each linked back to the source thread. #### How activity becomes a pattern. Each event is matched to your product by repo, package name, or docs URL. Errors are grouped by message, so ten people hitting the same wall show as one row with ten links. Agent-authored pull requests and issues are flagged, so you can separate human adoption from agent usage. Grouped friction feeds the Insights queue and the Stats rollup. #### Frequently Asked Questions - **Q: Does Context Buzz read private repos?** A: No. It reads public issues, discussions, and repository content only. Private repositories are never read. - **Q: How is a repo matched to my product?** A: By repository name, package name, or a reference to your docs URL. Each match is shown with the line that triggered it. - **Q: What is the agents toggle?** A: A filter that separates pull requests and issues written by coding agents from ones written by people, so you can read human adoption on its own. - **Q: Does it work for closed source products?** A: Yes, through references to your SDK, package name, or docs URLs in other people's public repositories. - **Q: Does EarShots reply inside these GitHub threads?** A: No draft is written for somebody else's repository. ### Competitor Signal (/product/competitor-signal) **What developers say about the tools you compete with.** Competitor Signal tracks public developer posts about the tools you compete with and sorts them into what people like, what they dislike, and who is comparing or switching. It gives technical marketing and sales the words developers use, not a sentiment score. *Summary:* One card per competitor: three lists (likes, dislikes, switching) with the source line under each item, and a comparison count. Once Agent Signal is connected, the card also shows how often coding agents chose them over you. #### One card per competitor, with the source behind every line. - **Likes, dislikes, switching**: Three lists per competitor, each item sourced to the original post, plus a running comparison count. - **Agent choices**: Once Agent Signal is connected, how often coding agents chose that competitor over your product. #### How a post becomes a like or a dislike. EarShots reads the post and extracts the specific claim, such as "pricing doubled" or "docs are better". Claims are grouped by meaning, not by keyword, so five phrasings of one complaint show once. Rival counts follow the plan: 3 on Starter, 5 on Growth, 15 on Enterprise, read over a 120 day window. #### Frequently Asked Questions - **Q: How many competitors can I track?** A: Your plan sets the count: 3 rivals on Starter, 5 on Growth, 15 on Enterprise. - **Q: Does it show sentiment scores?** A: No. It shows specific claims with the source under each one, not a single sentiment number. - **Q: Can I reply to a competitor's thread from EarShots?** A: EarShots reads and reports; it never comments or replies. - **Q: Is this available on the Starter plan?** A: Yes. Starter tracks up to 3 rivals. Agent Signal is on Growth and Enterprise. ### Ground Truth (/product/accuracy-maxxing) **Every claim about your product, checked against your docs.** Ground Truth collects claims about your product from AI answer engines, tutorials, comparison articles, and round-ups, and checks each claim against your documentation. It shows docs and developer relations teams which claims are wrong, which are stale, and which docs page would fix them. *Summary:* Left, claims grouped by source: Perplexity, ChatGPT, Google AI Overviews, blogs, and agent-authored pages. Right, each claim with a verdict (accurate, stale, wrong), the docs passage that contradicts it, and a suggested correction. #### Every claim, sourced and checked. - **Claims by source**: Perplexity, ChatGPT, Google AI Overviews, blogs, and agent-authored pages, listed separately. - **Verdict and correction**: Accurate, stale, or wrong, with the docs passage that contradicts the claim and a suggested correction. #### How a claim is checked. EarShots extracts the claim as a single sentence, searches your sources for the matching fact, and grades the gap. Claims from muted round-ups and directories feed in automatically. Corrections are drafts for your docs team, not posts. #### A person approves every correction. EarShots never edits your docs. #### Frequently Asked Questions - **Q: Which AI engines are checked?** A: Perplexity, ChatGPT, and Google AI Overviews, alongside blogs, tutorials, and agent-authored pages. - **Q: How often are claims rechecked?** A: Each sync re-reads connected sources, so a claim's verdict updates as your documentation changes. - **Q: Can EarShots fix my docs?** A: No. It drafts a correction pointing at the passage that is wrong. Your docs team commits the change. - **Q: What counts as a stale claim?** A: A claim that was true and now conflicts with a newer documentation passage, such as an old rate limit or a renamed feature. ### Stats (/product/stats) **The numbers behind your listening.** Stats reports volume by day, mentions by channel and track, drafts approved, and median time to reply, across every EarShots surface. Marketing, support, and founders read it for the weekly update. *Summary:* Five charts, one filter row for workspace, window, and channel, and an export. Volume is mentions after muting, and response time runs from post to approved reply. #### Volume, channel mix, and response time. - **Volume and channels**: Mentions by day, by channel, and by track, filtered by workspace and time window. - **Response time**: Median time from post to approved reply, plus drafts approved and the muted rate with its reason mix. #### What each number means. Volume is mentions after muting. Response time is measured from post to approved reply. Muted rate is shown with its reason mix, so a rising wrong-entity count tells you to add an exclusion. Windows: 7, 30, and 90 days. #### Frequently Asked Questions - **Q: Can I export Stats?** A: Yes, as a file you can attach to a weekly update. - **Q: How far back does history go?** A: Seven, thirty, and ninety day windows, filterable by workspace and channel. - **Q: Are muted items counted in volume?** A: No. Volume counts mentions after muting, and muted items are shown separately with their reasons. - **Q: Can I share a Stats view outside my workspace?** A: Export the view and share the file. Stats itself stays scoped to your workspace. ### Agent Signal (/product/agent-signal) **What coding agents decide about your product.** Agent Signal shows what AI coding agents say and do about your product: the questions they post, the libraries they choose, the docs they fail on, and the fixes they share. Product and developer relations teams use it to see where documentation fails an agent reader. *Summary:* Left, what agents are choosing: chose you, chose a competitor, fix shared. Right, where agents get stuck: docs failed, deprecated usage, open questions. Below, a matrix of which agent wrote and which agent reviewed, by repo. #### What agents choose, and where they get stuck. - **What agents are choosing**: Chose you, chose a competitor, or fix shared, each with the line that triggered the reading. - **Where agents get stuck**: Docs failed, deprecated usage, and open questions, each linked to the original post or pull request. #### Where the signal comes from. GitHub agent-authored pull requests and reviews, Moltbook, MoltOverflow, Mozilla cq, and Clawstr are read on every pull. Stack Overflow for Agents waits on a network key, and trace corpora wait on a published dataset. Each source is labeled, and trace data is marked as a sample, never a census. #### Agents and humans are scored apart. Every mention EarShots scores carries a flag for who wrote it: a person or a coding agent. The Sentiment report splits the two, and this page keeps the weekly trend side by side. When agent opinion drifts from human opinion, that gap usually means your docs, not your product, and it is fixable. #### A person reviews every insight. EarShots never posts on agent networks. #### Frequently Asked Questions - **Q: Which agent networks does EarShots read?** A: GitHub agent activity, Moltbook, MoltOverflow, Mozilla cq, and Clawstr on every pull. Stack Overflow for Agents and trace corpora are added once each source is available. - **Q: How do you know a pull request was written by an agent?** A: Either the account is on the agent roster, or the body carries a phrase the tool writes into its own output, such as a co-author trailer. A branch name alone never counts. - **Q: Does EarShots post on Moltbook or Stack Overflow for Agents?** A: No. It reads and reports what an agent already did in public, and links to the original. - **Q: Is trace data representative?** A: No. It is opt-in and labeled as a sample, never a census of agent behavior. ### Launch Planner (/product/launch-planner) **Know how a launch landed while it is still live.** Launch Planner watches public conversation about a release you name. Set the launch date and a window of 7, 14, or 30 days, and every post that lands inside it is read, counted, and grouped. The reaction is on the screen while the launch is still running, not in a retrospective a month later. *Summary:* Setup takes four fields: launch name, version, launch date and time, and how long to watch, plus a short note on what shipped. The results panel shows posts read per day, a reaction read across praise, new demand, questions, and complaints, and a link to the original post behind every number. #### What you see - **Setup**: Name the release, pick the window, describe what shipped in a sentence. Nothing else is needed to start watching. - **Daily volume**: Posts read per day inside the window with a running total. A launch that spikes on day one and goes quiet by day four looks different from one that builds. - **Reaction read**: Praise, new demand, questions, and complaints as shares of what was read, each opening the posts behind it. #### How it works A launch is a filter with a clock attached. EarShots reads every mention that names your product inside the window, applies the same classification the rest of the workspace uses, and reports the tally. Posts that arrive after the window closes are still in Insights, they just are not part of this read. #### Frequently Asked Questions - **Q: What counts as part of a launch?** A: A public post that names your product and lands inside the window you set. The window starts at the launch date and time you entered and runs for the length you picked. - **Q: What happens when the window closes?** A: The read stays in the workspace with its posts. Later mentions still appear in Insights, they are not added to that launch's tally. - **Q: Does EarShots announce the launch or reply to anyone?** A: No. It reads public posts and reports what it found. Drafts it prepares sit in the review queue until a person approves them. - **Q: Which plans include Launch Planner?** A: Growth and Enterprise. Starter does not include launch windows. ### CLI and MCP (/product/cli-mcp) **Work the queue from a terminal or an agent session.** EarShots ships a command line tool and an MCP server that read the same records the app shows. List mentions, open one post with its reasoning and citations, and store a reply draft without leaving the shell, or hand the same tools to an agent already doing the work. *Summary:* The CLI is @earshotsdev/cli. You sign in once, the session lives in ~/.earshots, and the commands cover insights, mentions, single posts, sources, Context Buzz, Stats, Launch Reactions, and drafts. The MCP server exposes the same data as tools an agent can call, including save_reply_draft, which stores a draft for a person to review. #### What you see - **The CLI**: Commands across the queue: earshots insights, mentions, show, sources, buzz, stats, launch, and draft. Output is plain text by default and structured with --json. - **The MCP server**: Tools that read mentions, insights, sentiment, Context Buzz, launch reactions, and competitor signal, plus save_reply_draft for storing a draft against a post. - **The guardrail**: No command and no tool posts a reply, opens a thread, or messages an author. Writing to a network is not part of the surface. #### How it works Sign in with earshots login: the browser opens, you approve once, and the token is stored locally and refreshed on its own. Commands then run against the active workspace, the same one the app has open, and you switch with earshots workspaces. #### Frequently Asked Questions - **Q: How do I install the CLI?** A: npm install -g @earshotsdev/cli, then run earshots login. It needs Node 20 or newer. - **Q: Does an agent need its own key?** A: An agent session works with the same workspace access as the signed-in user, so plan limits and review rules apply to it the same way. - **Q: Can an agent send the reply?** A: No. The tools read data and store drafts. Sending stays with a person in the app. - **Q: Are any tools limited by plan?** A: get_launch_reactions is on Growth and Enterprise. get_competitor_signal follows the rival count on the workspace plan. The rest follow the workspace plan the same way the app does. ### Knowledge Hub (/product/knowledge-hub) **Drafts that sound like your team wrote them.** The Knowledge Hub holds what your agents must get right about your business: who you sell to, what you are working toward, how you sound, what you never say, and which things share your name but are not you. Every sort, draft, and check reads from it. *Summary:* The Knowledge Hub is a versioned memory inside EarShots. You add context by pasting text, writing a rule, or pointing at a page. The agents use it when they sort mentions, draft replies, and check their own work. Every change keeps the version before it, a person confirms what becomes active, and you can delete any item at any time. #### Six kinds of knowledge, written plainly. - Who you sell to: your ideal customer profiles, in your words. Relevance is scored against these, not a generic idea of a developer. - Objectives: a launch, a new segment. Mentions that bear on an objective rank higher. - Traits: how the product must come across. Voice and positioning rules shape every draft. - Guardrails: what you never say. A guardrail outranks everything else, and a draft that breaks one is stopped with the reason attached. - Capabilities: what the product actually does, so drafts describe the product as it is. - Name collisions: things named like you that are not you. Those posts are screened out. #### Read before every decision. Checked after every draft. - Screening, Ears agent: judges each mention against your confirmed ICPs and name collisions before it reaches the queue, so lookalikes never become work. - Drafting, Ears agent: reads the relevant slice of the Knowledge Hub alongside your documentation, writes the draft, and cites both. - Checking, Ears agent then you: the agent tests every draft against your guardrails. A person reads, edits, approves, or skips it. Nothing is sent without that approval. - Learning, Ears agent then you: your edits and skips become proposed lessons. A person confirms or rejects each one before the agent uses it. #### Versioned, inspectable, removable. Nothing enters the active Knowledge Hub without a person confirming it. Extracted items start as candidates you approve or reject. Every change keeps the version before it, so you can see what the agents knew at any point. Delete an item and its whole history goes with it, and the agents stop using it immediately. #### Frequently Asked Questions - **Q: How does context get into the Knowledge Hub?** A: Three ways: paste text such as a doc or positioning note, write a rule directly, or point at a page such as your docs or site. EarShots reads them into candidate items that a person confirms before the agents use them. - **Q: Can the agents change the Knowledge Hub on their own?** A: No. Agents propose candidates and lessons from outcomes. A person approves or rejects them. Confirmed rules can only be changed by a person, and every change is versioned. - **Q: What happens when I delete something?** A: The item, all of its historical versions, and every relationship touching it are removed. The agents no longer use it from the next decision onward. - **Q: Why do name collisions matter?** A: Common product names pull in posts about bands, coins, and unrelated tools. The Knowledge Hub stores each namesake with the lines that distinguish it, and those posts are screened out before they reach your queue. ### AEO / GEO platform (/aeo-geo-platform) **Track Your Visibility Across AI Answers, Then Act on It** EarShots builds an agentic AEO / GEO prompt universe to explore new prompts across your category, grounded in real user questions, product friction, and buying signals. Track your visibility, mention rate, and citation share across the prompts your market actually runs, then secure AEO citations directly in the articles feeding those answers. *Summary:* Each tracked prompt runs daily or weekly on ChatGPT, Claude, Gemini and Google AI Overviews. Every answer is checked for real mentions of your product, not namesakes. You get a rank score per prompt and per engine, trendlines for mentions and citations, and recommendations for any gap. #### Know what to fix for each prompt. On any tracked prompt, EarShots reads the AI answers, who gets mentioned and cited, and what your docs cover, then gives recommendations to win that prompt. EarShots drafts replies on X, LinkedIn and Reddit for your approval. #### Frequently Asked Questions - **Q: What is agentic AEO?** A: Agentic AEO means AI agents do the tracking and analysis work, not just dashboards. With EarShots, an agent connected over MCP, the REST API or the CLI can build your prompt universe, read every engine's answers and citations, and tell you which prompts to fix first. Recommendations and drafts stay drafts until a person approves them. - **Q: What is an AEO platform for dev tools?** A: An AEO (answer engine optimization) platform for dev tools tracks whether ChatGPT, Claude, Gemini and Google AI Overviews name and cite your library, API or CLI for the prompts developers and coding agents ask, and shows which docs, repos and threads those answers rely on. - **Q: How should a startup approach AEO?** A: Start with 20 to 50 prompts taken from real customer questions, track them on every engine, and work on the ones where no product is named consistently. Then get cited in the articles engines already quote, which is what [Ears Cite](/aeo-citations) does. - **Q: Which AI engines does Prompt Universe track?** A: ChatGPT with web search, Claude with web search, Gemini, and Google AI Overviews. - **Q: What is query fan-out and why does it matter for AEO?** A: When someone asks an AI engine one question, the engine often fans it out into several related searches before answering. EarShots shows you the fan-out queries behind each prompt, so you can see the full set of questions an engine researches and make sure your docs and sources cover them, not just the headline prompt. - **Q: What is the difference between AEO and GEO?** A: AEO (answer engine optimization) is about getting named in AI answers. GEO (generative engine optimization) is about getting your sources cited inside the generated answer itself. EarShots tracks both: mention rate tells you if you are named, citation share tells you if your pages are the source. - **Q: How do I get my product cited by ChatGPT and Claude?** A: Engines cite pages that already answer the question clearly: documentation, comparison articles and community threads. EarShots shows which sources each engine trusts for your prompts, and [Ears Cite](/aeo-citations) reaches out to the authors of the articles engines already quote to include your product where it fits. - **Q: How do I measure share of voice in AI answers?** A: Track a fixed set of prompts over time and compare how often each competitor is named and cited per engine. EarShots computes this automatically across your prompt universe, with trendlines per engine, so you can see whether your share is growing or a competitor is taking it. - **Q: How often do prompts refresh?** A: You choose daily or weekly for each prompt. - **Q: What is the difference between a mention and a citation?** A: A mention means the answer mentions your product. A citation means the answer links your site as a source. Both are tracked separately. - **Q: How many prompts can I track?** A: Starter tracks 100 and Growth tracks 250. Enterprise tracks prompts on credits, sized with the EarShots team for your volume. - **Q: Does EarShots publish anything to my docs?** A: No. EarShots never edits your site. It drafts replies on X, LinkedIn and Reddit for your approval, and gives recommendations for your docs. --- ## Industry Definitions & GTM Solutions ### Industries / Video game studios and publishers (/industries/video-games) **Social listening for video game companies** For studios, publishers and gaming platforms. Review public player feedback and discovery questions by title, platform and release, so community, product and marketing teams can choose what to address. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps video game studios and publishers teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide A publisher may need to compare reactions across titles. An independent studio may need to prioritize a few unanswered purchase questions. A platform team needs to distinguish player needs from developer integration problems. #### Help players find the right game in AI answers A player can ask an AI assistant which co-op game fits a short evening, whether friends can play across platforms or which title offers specific accessibility features. Review whether your game appears, how it is described and which sources support the recommendation before the player reaches a store or review page. Gaming assistants already offer game recommendations and help[1](#industry-source-1). Broader publisher research reports declining search referrals[2](#industry-source-2), although it does not isolate gaming audiences. Track visibility in the answer alongside visits to your own pages. - Turn questions from listening into prompts in [Prompt Universe](/aeo-geo-platform), such as: Which co-op games suit short evening sessions and support cross-play? - Read the answer and its cited sources. Check platform combinations, current features and the release the answer describes with [Ground Truth](/product/accuracy-maxxing), then improve game feature pages, cross-play and accessibility FAQs, and dated patch information where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### Start with these reviews - **Mentions**: Review the player need behind a question or patch reaction. [Explore Mentions](/product/agentic-social-listening). Questions, friction, buying signals and praise from connected public sources. - **Launch reactions**: Compare reactions to the release within its watch window. [Explore Launch reactions](/product/launch-planner). Public reactions in a chosen release watch window; availability follows your plan. - **Prompt Universe**: Which co-op games suit short evening sessions and support cross-play? [Explore Prompt Universe](/aeo-geo-platform). Saved AI answers, brand mentions and cited sources for the prompts you choose. #### What would make the signal useful? Players describing repeat sessions, recommending the game for a specific experience or returning after an update can support an audience-fit hypothesis. Check retention, wishlists, purchases and repeat play in your own game and store analytics. Public enthusiasm and AI mentions do not establish sales or product-market fit. #### Terms in context - [Cross-play](/glossary#cross-play) - [Buying signal](/glossary#buying-signal) - [Sentiment score](/glossary#sentiment-score) - [Product-market fit](/glossary#product-market-fit) - [Answer engine optimization](/glossary#answer-engine-optimization) #### Related industries - [Game engines and realtime infrastructure](/industries/gaming-realtime) - [Communications APIs](/industries/communications-apis) - [Ecommerce and storefront platforms](/industries/ecommerce-platforms) #### Sources 1. [Gaming assistants already offer game recommendations and help](https://news.xbox.com/en-us/2025/09/18/gaming-copilot-xbox-pc-mobile/) 2. [Broader publisher research reports declining search referrals](https://reutersinstitute.politics.ox.ac.uk/journalism-media-and-technology-trends-and-predictions-2026) #### Key Questions - **Q: Which sources should we start with?** A: Start with public Reddit, X and web discussions about your titles. Use LinkedIn and public GitHub where developer platform questions matter. This workflow does not establish direct Steam, Discord, console-store or private community coverage. - **Q: What does AI search visibility tell us?** A: Start with a question relevant to your audience, such as: Which co-op games suit short evening sessions and support cross-play? Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Developer tools and APIs (/industries/developer-tools) **Social listening for developer tools and APIs** For teams selling tools and application programming interfaces (APIs) to developers. Review integration questions, public repository usage and competing tools with the source behind each finding. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps developer tools and apis teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Does the next release need a product fix, a clearer example or a better explanation of who the tool fits? Product, developer relations and support can examine the same evidence before choosing the work. #### Help developers find a tool that fits their integration A developer can ask an AI assistant which API fits a language, framework or deployment constraint. Review whether answers describe your supported integrations accurately and point to useful documentation. - Read the answer and its cited sources. Check supported versions, setup requirements and working examples with [Ground Truth](/product/accuracy-maxxing), then improve language-specific integration examples and compatibility pages where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Independent teams returning with a second integration, sharing working implementations or recommending the tool for a specific job can support an adoption hypothesis. Check activation and repeat usage in your own product analytics. Repeated public questions alone may indicate unresolved onboarding friction. #### Terms in context - [SDK](/glossary#sdk) - [Context Buzz](/glossary#context-buzz) - [Buying signal](/glossary#buying-signal) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Testing and quality assurance](/industries/testing-qa) - [Open source companies](/industries/open-source) - [Agents and AI coding tools](/industries/ai-coding-agents) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / AI and language-model infrastructure (/industries/ai-infrastructure) **Social listening for AI infrastructure** For model providers, inference platforms and AI tooling teams. Review what builders are trying to run, the limits they encounter and the alternatives they consider. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps ai and language-model infrastructure teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide A complaint about latency means little without the workload. Bring product and technical marketing together around the model, deployment constraints and evaluation method before changing positioning or priorities. #### Show where your platform fits a real AI workload Model and infrastructure recommendations depend on workload, latency, deployment and cost assumptions. Track those questions to find where your platform is omitted or described without the constraints buyers need. - Read the answer and its cited sources. Check model versions, deployment options and benchmark conditions with [Ground Truth](/product/accuracy-maxxing), then improve workload guides, evaluation methods and current model-limit pages where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Repeat deployments for a named workload and independent reports of useful results are more informative than benchmark attention. Validate with workload retention, paid consumption and evaluation results from customers. A public benchmark does not establish production suitability. #### Terms in context - [Inference](/glossary#inference) - [Context Buzz](/glossary#context-buzz) - [Citation](/glossary#citation) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Agents and AI coding tools](/industries/ai-coding-agents) - [Data and analytics platforms](/industries/data-platforms) - [Databases and storage](/industries/databases-storage) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Open source companies (/industries/open-source) **Social listening for open source companies** For maintainers and companies with a hosted or commercial offering. Read public implementation questions alongside requests for managed operations, team controls and support. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps open source companies teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Decide which problems belong in the project and which suggest a commercial need. Maintainer activity, stars and a request for a hosted service answer different questions. #### Clarify when the project or hosted service fits AI answers can blur the distinction between a community project and its commercial offering. Check whether builders see the right self-hosting, managed-service and support options for their needs. - Read the answer and its cited sources. Check license, edition boundaries and maintenance responsibilities with [Ground Truth](/product/accuracy-maxxing), then improve self-hosted versus managed-service comparisons and edition guides where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Independent production use and specific requests for a paid operational service can support a commercial-fit hypothesis. Check retained usage and paid conversion separately. Stars, forks and contributor activity do not establish willingness to pay. #### Terms in context - [Self-hosted](/glossary#self-hosted) - [Context Buzz](/glossary#context-buzz) - [Buying signal](/glossary#buying-signal) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Developer tools and APIs](/industries/developer-tools) - [Databases and storage](/industries/databases-storage) - [Cloud and DevOps platforms](/industries/cloud-devops) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Cloud and DevOps platforms (/industries/cloud-devops) **Social listening for cloud and DevOps platforms** For hosting, deployment and infrastructure teams. Review migration discussions with the workload, operating costs and deployment constraints that give them meaning. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps cloud and devops platforms teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Decide whether the obstacle is reliability, operating effort, price or documentation. An outage thread and a considered migration plan need different follow-up. #### Make migration trade-offs clear in AI answers Teams can ask AI to shortlist deployment platforms before reading vendor comparisons. Review which alternatives appear and whether your workload, region and operating constraints are represented accurately. - Read the answer and its cited sources. Check regions, deployment requirements and pricing assumptions with [Ground Truth](/product/accuracy-maxxing), then improve migration guides and workload-specific platform comparisons where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Teams describing completed migrations and sustained use for a clear workload can support a fit hypothesis. Confirm retention, workload growth and operating outcomes in customer records. Public switching intent is not a completed migration. #### Terms in context - [Migration](/glossary#migration) - [Buying signal](/glossary#buying-signal) - [Competitor Signal](/glossary#competitor-signal) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Platform engineering](/industries/platform-engineering) - [Observability](/industries/observability) - [Mobile and edge platforms](/industries/mobile-edge) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Security and DevSecOps (/industries/security) **Social listening for security platforms** For application security and developer security teams. Review public integration friction and claims about your coverage against the documentation and evidence available. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps security and devsecops teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Help product and security specialists distinguish an integration obstacle, a coverage question and an allegation that needs investigation. A public report cannot confirm a vulnerability by itself. #### Keep security recommendations within your actual coverage AI-assisted shortlists can repeat an outdated coverage or integration claim. Review the saved answer and supporting references so prospective buyers can assess your product against their own requirements. - Read the answer and its cited sources. Check product version, coverage boundaries and supporting references with [Ground Truth](/product/accuracy-maxxing), then improve coverage matrices, supported-integration pages and product-scope explanations where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Repeated use within engineering workflows and clear requests for broader team rollout can support an adoption hypothesis. Validate deployment retention, actual coverage and purchasing requirements with customers. Public sentiment is not a security assessment. #### Terms in context - [Vulnerability](/glossary#vulnerability) - [Ground Truth](/glossary#ground-truth) - [Friction](/glossary#friction) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Identity and authentication](/industries/identity-auth) - [Platform engineering](/industries/platform-engineering) - [Testing and quality assurance](/industries/testing-qa) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Data and analytics platforms (/industries/data-platforms) **Social listening for data and analytics platforms** For data integration, orchestration and analytics teams. Review public reports of connector limits, schema changes and workload trade-offs with their original context. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps data and analytics platforms teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Decide which connector, documentation gap or workload limitation deserves attention. A small experiment and a production pipeline should not carry the same implication. #### Answer the pipeline questions behind platform selection Data teams can ask AI which platform supports a specific source, destination and freshness requirement. Find which connector questions your documentation leaves unresolved in captured answers. - Read the answer and its cited sources. Check connector support, schema behavior and freshness constraints with [Ground Truth](/product/accuracy-maxxing), then improve connector documentation and pipeline architecture guides where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Recurring production pipelines, new workloads from existing users and explicit requests for operational support can support a fit hypothesis. Confirm retained workloads and successful runs with product data. Public pipeline examples are a partial sample. #### Terms in context - [Data pipeline](/glossary#data-pipeline) - [Context Buzz](/glossary#context-buzz) - [Friction](/glossary#friction) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Databases and storage](/industries/databases-storage) - [AI and language-model infrastructure](/industries/ai-infrastructure) - [Observability](/industries/observability) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Platform engineering (/industries/platform-engineering) **Social listening for platform engineering tools** For internal developer portal and platform-tool vendors. Review public discussions about service ownership, paved paths and the work required to keep a platform useful. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps platform engineering teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Understand what the platform team wants to standardize and what application teams resist. Use that distinction to improve onboarding and explain the product's operating cost. #### Explain who your developer platform is built for An AI-generated portal shortlist may overlook the work needed to adopt and maintain a platform. Check whether answers describe the team model, supported workflows and operating responsibilities that matter to buyers. - Read the answer and its cited sources. Check team responsibilities, integrations and maintenance requirements with [Ground Truth](/product/accuracy-maxxing), then improve adoption guides, reference workflows and platform comparisons where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Several application teams using a common workflow and requesting additional services can support an expansion hypothesis. Confirm active team adoption and time spent maintaining the platform with customers. A portal installation does not establish internal adoption. #### Terms in context - [Internal developer platform](/glossary#internal-developer-platform) - [Context Buzz](/glossary#context-buzz) - [Friction](/glossary#friction) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Cloud and DevOps platforms](/industries/cloud-devops) - [Observability](/industries/observability) - [Security and DevSecOps](/industries/security) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Frontend and design systems (/industries/frontend-design-systems) **Social listening for frontend and design tools** For component libraries, build tools and design-system vendors. Review implementation questions about accessibility, framework compatibility and customization. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps frontend and design systems teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Distinguish a missing capability from an unclear example. Design, engineering and developer relations need the actual component, framework and interaction to choose a useful fix. #### Make framework and accessibility fit easier to evaluate Developers can ask AI for a component library that works with their framework and accessibility requirements. Review whether recommendations link to current, specific evidence instead of a generic feature list. - Read the answer and its cited sources. Check framework versions and documented interaction behavior with [Ground Truth](/product/accuracy-maxxing), then improve framework compatibility pages and accessible component examples where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Independent teams extending a library across applications and sharing maintained integrations can support an adoption hypothesis. Check continued use and upgrade behavior. Downloads and a popular demo do not establish sustained use. #### Terms in context - [Accessibility](/glossary#accessibility) - [Context Buzz](/glossary#context-buzz) - [Friction](/glossary#friction) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Developer tools and APIs](/industries/developer-tools) - [Testing and quality assurance](/industries/testing-qa) - [Ecommerce and storefront platforms](/industries/ecommerce-platforms) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Edge, IoT and hardware (/industries/edge-iot-hardware) **Social listening for IoT and hardware platforms** For device software development kit (SDK), firmware and connectivity teams. Review public hardware integration reports without losing the board revision, firmware or operating conditions. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps edge, iot and hardware teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Internet of Things (IoT) problems can depend on a specific device configuration. Help engineering and support identify what needs a reproduction before treating several reports as one defect. #### Help device teams discover compatible tooling Hardware recommendations need a device, firmware and connectivity context. Track those questions to find gaps between what your SDK supports and how AI answers describe it. - Read the answer and its cited sources. Check board revisions, firmware versions and connectivity limits with [Ground Truth](/product/accuracy-maxxing), then improve device compatibility tables and firmware integration guides where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Repeat deployments on a defined hardware configuration and requests for fleet support can support a commercial-fit hypothesis. Confirm active devices, retained deployments and support requirements with customers. A prototype repository does not establish a fleet rollout. #### Terms in context - [Firmware](/glossary#firmware) - [Context Buzz](/glossary#context-buzz) - [Friction](/glossary#friction) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Mobile and edge platforms](/industries/mobile-edge) - [Communications APIs](/industries/communications-apis) - [Cloud and DevOps platforms](/industries/cloud-devops) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Blockchain infrastructure (/industries/web3-infrastructure) **Social listening for blockchain infrastructure** For node, wallet, indexing and blockchain development-tool providers. Review integration friction and provider comparisons in the context of a specific network and application. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps blockchain infrastructure teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Distinguish a network incident, an integration problem and a provider limitation. The chain, service tier and workload determine which team should investigate. #### Put network and workload context into provider comparisons AI-generated infrastructure recommendations can be too broad for a particular network or application. Check the supported chains, request requirements and documentation behind the answer. - Read the answer and its cited sources. Check network availability, service limits and request behavior with [Ground Truth](/product/accuracy-maxxing), then improve network-support pages and application integration guides where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Maintained applications and repeat service usage for a named workload can support an adoption hypothesis. Check retained applications and paid usage. Token activity, community attention and a repository mention do not establish product fit. #### Terms in context - [Remote procedure call](/glossary#remote-procedure-call) - [Context Buzz](/glossary#context-buzz) - [Competitor Signal](/glossary#competitor-signal) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Developer tools and APIs](/industries/developer-tools) - [Identity and authentication](/industries/identity-auth) - [Databases and storage](/industries/databases-storage) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Developer marketing and DevRel agencies (/industries/devrel-agencies) **Social listening for developer marketing agencies** For developer relations (DevRel), technical content and developer marketing agencies. Organize listening around each client's product, audience and decision, with separate workspace context. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps developer marketing and devrel agencies teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Show a client which questions need an answer, which product obstacles recur and which comparisons deserve better material. Link recommendations to the original sources rather than presenting post volume as impact. #### Give clients an AI discovery brief they can act on Add the buyer questions AI answers to each client's listening brief. Show which competitors and sources appear, which product claims need review and which useful content would address the gap. - Read the answer and its cited sources. Check client context, captured answers and the source behind each recommendation with [Ground Truth](/product/accuracy-maxxing), then improve client-specific comparison briefs and evidence-backed content plans where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Recurring client use of source-backed briefs and expansion to additional products can support a service-fit hypothesis. Confirm renewals, accepted recommendations and outcomes with each client. Report conversation activity separately from qualified demand or revenue. #### Terms in context - [Developer relations](/glossary#developer-relations) - [Workspace](/glossary#workspace) - [Buying signal](/glossary#buying-signal) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Developer tools and APIs](/industries/developer-tools) - [B2B software platforms](/industries/b2b-saas-platforms) - [Open source companies](/industries/open-source) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Fintech and payments APIs (/industries/fintech-payments) **Social listening for fintech and payment APIs** For payment, ledger and financial integration platforms. Review public developer questions about webhooks, retries and test environments with the documentation behind a possible answer. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps fintech and payments apis teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Help product and developer support distinguish a misunderstood integration contract from behavior that needs investigation. A sandbox report and a production payment failure need different handling. #### Help buyers evaluate the right payment integration An AI shortlist needs to distinguish recurring billing, platform payouts and other payment flows. Check whether answers describe your actual integration requirements and direct buyers to current product information. - Read the answer and its cited sources. Check API versions, available payment flows and product scope with [Ground Truth](/product/accuracy-maxxing), then improve payment-flow guides, integration requirements and supported-feature pages where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Repeat integrations, explicit production rollout plans and demand for additional payment flows can support a fit hypothesis. Confirm retained integrations and successful transaction usage internally. Public reports do not verify transaction outcomes or regulatory suitability. #### Terms in context - [Webhook](/glossary#webhook) - [Buying signal](/glossary#buying-signal) - [Friction](/glossary#friction) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Ecommerce and storefront platforms](/industries/ecommerce-platforms) - [Identity and authentication](/industries/identity-auth) - [Developer tools and APIs](/industries/developer-tools) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Ecommerce and storefront platforms (/industries/ecommerce-platforms) **Social listening for ecommerce platforms** For commerce, checkout and storefront platform teams. Review what merchants, agencies and integrators need from a migration before deciding which claims or examples to improve. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps ecommerce and storefront platforms teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Separate merchant requirements from an agency's implementation constraints. Checkout behavior, catalog complexity and operating effort can point to different product and documentation work. #### Show which storefront requirements your platform meets Merchants and agencies can ask AI to compare commerce platforms for an existing storefront. Review whether answers explain checkout, catalog and customization trade-offs accurately enough to support a useful evaluation. - Read the answer and its cited sources. Check checkout constraints, integration support and implementation requirements with [Ground Truth](/product/accuracy-maxxing), then improve migration guides and checkout or storefront capability comparisons where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Agencies selecting the platform for additional stores and merchants expanding their use can support a fit hypothesis. Confirm retained stores and repeat implementation work. A recommendation thread cannot establish conversion lift or merchant revenue. #### Terms in context - [Headless commerce](/glossary#headless-commerce) - [Buying signal](/glossary#buying-signal) - [Competitor Signal](/glossary#competitor-signal) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Fintech and payments APIs](/industries/fintech-payments) - [Frontend and design systems](/industries/frontend-design-systems) - [Marketing and growth automation](/industries/martech-automation) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Game engines and realtime infrastructure (/industries/gaming-realtime) **Social listening for game engines and multiplayer tools** For engine, multiplayer, voice and game-backend vendors. Review studio integration questions with their engine version, target platforms and multiplayer requirements. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps game engines and realtime infrastructure teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Decide whether studios need a better integration example, a product investigation or a clearer account of the workloads you support. Keep developer infrastructure needs separate from player feedback about a title. #### Help studios find infrastructure for their game A studio can ask AI which multiplayer backend fits its engine, platforms and session design. Track those questions and inspect whether recommendations explain the constraints behind a useful choice. - Read the answer and its cited sources. Check engine versions, target platforms and session constraints with [Ground Truth](/product/accuracy-maxxing), then improve engine integration guides and multiplayer architecture comparisons where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Studios using the service across releases or choosing it again for a second project can support a fit hypothesis. Confirm retained production titles and service usage with customers. A prototype and a claimed latency improvement do not establish production fit. #### Terms in context - [Netcode](/glossary#netcode) - [Context Buzz](/glossary#context-buzz) - [Buying signal](/glossary#buying-signal) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Video game studios and publishers](/industries/video-games) - [Communications APIs](/industries/communications-apis) - [Cloud and DevOps platforms](/industries/cloud-devops) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / B2B software platforms (/industries/b2b-saas-platforms) **Social listening for B2B SaaS platforms** For business software with integrations and extensible workflows. Review what administrators and integrators need when they compare, configure or replace a platform. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps b2b software platforms teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Separate a user's missing workflow from an administrator's rollout requirement. Product and commercial teams need both to judge whether a request fits the product and its intended customer. #### Appear in the workflow comparisons buyers actually ask for Business buyers can describe a workflow to an AI assistant instead of searching for a software category. Review whether your product is considered for that job and whether the answer explains its integration and administration requirements. - Read the answer and its cited sources. Check integration support, administrative controls and edition limits with [Ground Truth](/product/accuracy-maxxing), then improve use-case pages and workflow-specific replacement comparisons where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Repeated use across teams and requests to expand a working workflow can support a fit hypothesis. Check active account retention, expansion and renewal evidence. A public replacement request is not a qualified opportunity or a lost renewal. #### Terms in context - [Software as a service](/glossary#software-as-a-service) - [Buying signal](/glossary#buying-signal) - [Competitor Signal](/glossary#competitor-signal) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Customer support platforms](/industries/customer-support-cx) - [Marketing and growth automation](/industries/martech-automation) - [Identity and authentication](/industries/identity-auth) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Identity and authentication (/industries/identity-auth) **Social listening for identity and authentication platforms** For authentication, single sign-on (SSO) and identity platform teams. Review public integration questions with the tenant configuration, framework and account requirements attached. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps identity and authentication teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Decide whether developers need clearer setup guidance or an investigation into the authentication flow. Enterprise account requirements and end-user login failures require different evidence. #### Make authentication requirements clear before evaluation Developers can ask AI to compare identity providers for an enterprise application. Review whether answers explain your single sign-on, organization and integration capabilities with the right plan and configuration context. - Read the answer and its cited sources. Check identity providers, tenant configuration and plan boundaries with [Ground Truth](/product/accuracy-maxxing), then improve single sign-on guides and enterprise capability comparisons where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Production integrations retained through upgrades and requests for additional organizations can support a fit hypothesis. Confirm active applications and expansion in account records. Public login complaints do not confirm a security defect or customer churn. #### Terms in context - [Single sign-on](/glossary#single-sign-on) - [Friction](/glossary#friction) - [Ground Truth](/glossary#ground-truth) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Security and DevSecOps](/industries/security) - [B2B software platforms](/industries/b2b-saas-platforms) - [Fintech and payments APIs](/industries/fintech-payments) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Communications APIs (/industries/communications-apis) **Social listening for communications APIs** For email, messaging, voice and video platform teams. Review public integration reports with the channel, region and callback behavior that make them actionable. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps communications apis teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Distinguish implementation errors, delivery expectations and service behavior. Developer support needs the original report before preparing an answer or escalating an investigation. #### Connect AI recommendations to the right communication flow An application team needs different evidence for transactional email, messaging and live voice. Track questions by channel and inspect whether the answer describes your supported regions and integration requirements accurately. - Read the answer and its cited sources. Check channel availability, delivery constraints and SDK support with [Ground Truth](/product/accuracy-maxxing), then improve channel-specific integration guides and region-support pages where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Repeat use across communication flows and requests for new regions or channels can support an expansion hypothesis. Confirm retained usage and delivery outcomes in service data. Public sentiment cannot establish deliverability or call quality. #### Terms in context - [Deliverability](/glossary#deliverability) - [Friction](/glossary#friction) - [Buying signal](/glossary#buying-signal) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Developer tools and APIs](/industries/developer-tools) - [Game engines and realtime infrastructure](/industries/gaming-realtime) - [Customer support platforms](/industries/customer-support-cx) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Customer support platforms (/industries/customer-support-cx) **Social listening for customer support platforms** For help desk, support inbox and customer experience (CX) platform teams. Review how administrators and developers describe routing, integrations and the tools they are replacing. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps customer support platforms teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Help product and implementation teams understand whether an obstacle belongs in routing rules, integration guidance or the product. Separate the administrator's problem from the end customer's complaint. #### Answer the support-workflow questions behind the shortlist Support teams can ask AI which platform fits their channels, routing and issue-tracking needs. Review whether your product is described in the context of that workflow and which sources the answer uses. - Read the answer and its cited sources. Check supported channels, permissions and handoff behavior with [Ground Truth](/product/accuracy-maxxing), then improve routing guides and integration-specific support workflow pages where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Teams extending a working setup to more support groups can support an expansion hypothesis. Check active usage, renewals and operational outcomes with customers. A favorable post cannot establish improved resolution time. #### Terms in context - [Customer experience](/glossary#customer-experience) - [Friction](/glossary#friction) - [Buying signal](/glossary#buying-signal) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [B2B software platforms](/industries/b2b-saas-platforms) - [Communications APIs](/industries/communications-apis) - [Marketing and growth automation](/industries/martech-automation) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Marketing and growth automation (/industries/martech-automation) **Social listening for marketing automation platforms** For marketing automation, analytics and experimentation platforms. Review integration friction and attribution questions with the event definitions and workflow in view. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps marketing and growth automation teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Decide whether teams need a better integration, clearer measurement guidance or a different product capability. A missing event and a disputed attribution model require different fixes. #### Clarify the measurement and automation jobs you support AI recommendations can group very different analytics and automation products together. Track the event, attribution and campaign questions that match your product, then review where answers confuse its capabilities. - Read the answer and its cited sources. Check event definitions, reporting assumptions and automation limits with [Ground Truth](/product/accuracy-maxxing), then improve event integration guides and measurement-method explanations where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Teams adding repeat campaigns or workflows to an established implementation can support a fit hypothesis. Confirm retained workflows and account expansion. Public engagement and reported campaign results do not establish incremental revenue. #### Terms in context - [Attribution](/glossary#attribution) - [Buying signal](/glossary#buying-signal) - [Citation](/glossary#citation) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [B2B software platforms](/industries/b2b-saas-platforms) - [Ecommerce and storefront platforms](/industries/ecommerce-platforms) - [Data and analytics platforms](/industries/data-platforms) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Databases and storage (/industries/databases-storage) **Social listening for databases and storage platforms** For database, storage, cache and driver teams. Review public migration discussions and implementation reports with the data model and operating requirements attached. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps databases and storage teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Help product and developer teams separate a workload mismatch, a configuration problem and a missing capability. A benchmark result needs its method before it can inform positioning. #### Keep the workload attached to database recommendations A useful AI database comparison needs the data model, query patterns and operational constraints. Review whether your product is recommended for appropriate workloads and whether the cited benchmarks support the claims. - Read the answer and its cited sources. Check database versions, topology and benchmark methodology with [Ground Truth](/product/accuracy-maxxing), then improve workload guides and reproducible benchmark or migration pages where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Sustained production workloads and repeat selection for new applications can support a fit hypothesis. Confirm workload retention, upgrades and paid adoption. Benchmark attention and download counts are insufficient evidence. #### Terms in context - [Workload](/glossary#workload) - [Context Buzz](/glossary#context-buzz) - [Competitor Signal](/glossary#competitor-signal) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Data and analytics platforms](/industries/data-platforms) - [Open source companies](/industries/open-source) - [AI and language-model infrastructure](/industries/ai-infrastructure) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Observability (/industries/observability) **Social listening for observability platforms** For tracing, log, metric and error-monitoring teams. Review public instrumentation questions and cost comparisons with the configuration and workload that explain them. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps observability teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Help product and developer support decide whether a missing trace needs configuration guidance or a product investigation. Keep pricing comparisons tied to the volume and retention assumptions. #### Explain instrumentation and cost assumptions in AI comparisons Teams can ask AI which monitoring platform fits their stack and telemetry needs. Inspect whether your integrations and pricing assumptions are represented accurately before deciding which documentation needs improvement. - Read the answer and its cited sources. Check SDK support, sampling, retention and volume assumptions with [Ground Truth](/product/accuracy-maxxing), then improve instrumentation guides and workload-based cost explanations where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Teams extending a working instrumentation setup to additional services can support an expansion hypothesis. Confirm retained services and useful telemetry internally. Public bill comparisons do not establish lower total cost. #### Terms in context - [Instrumentation](/glossary#instrumentation) - [Context Buzz](/glossary#context-buzz) - [Friction](/glossary#friction) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Cloud and DevOps platforms](/industries/cloud-devops) - [Platform engineering](/industries/platform-engineering) - [AI and language-model infrastructure](/industries/ai-infrastructure) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Testing and quality assurance (/industries/testing-qa) **Social listening for testing and QA tools** For testing frameworks and quality assurance (QA) platforms. Review public failure reports with the runner, browser and environment before choosing the next investigation. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps testing and quality assurance teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Separate test instability, environment differences and product defects. Engineering and developer support need a reproducible case before promising a fix or comparing tools. #### Help teams choose testing tools for their environment AI-generated shortlists can overlook a runner, framework or browser constraint. Track the testing questions relevant to your product and inspect whether answers point to a working setup and an appropriate use case. - Read the answer and its cited sources. Check framework versions, execution environment and supported test types with [Ground Truth](/product/accuracy-maxxing), then improve runner compatibility pages and reproducible test examples where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Teams keeping the tool in release checks and expanding it to more projects can support an adoption hypothesis. Confirm continued runs and customer-reported testing outcomes. A coverage claim or a tool roundup is not proof of product impact. #### Terms in context - [Continuous integration](/glossary#continuous-integration) - [Friction](/glossary#friction) - [Context Buzz](/glossary#context-buzz) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Developer tools and APIs](/industries/developer-tools) - [Frontend and design systems](/industries/frontend-design-systems) - [Agents and AI coding tools](/industries/ai-coding-agents) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Mobile and edge platforms (/industries/mobile-edge) **Social listening for mobile and edge platforms** For mobile software development kit (SDK), backend and edge-runtime teams. Review public performance and compatibility questions in the environment where the developer encountered them. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps mobile and edge platforms teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Decide whether a report needs a device-specific reproduction, an upgrade guide or a runtime investigation. Separate application behavior from an unverified claim about your SDK. #### Make device and runtime fit visible in AI answers Developers can ask AI for a mobile SDK or backend that meets offline, device and runtime requirements. Review whether recommendations reflect your current compatibility information and supported operating conditions. - Read the answer and its cited sources. Check operating-system versions, runtime support and network assumptions with [Ground Truth](/product/accuracy-maxxing), then improve device compatibility pages and offline or runtime integration guides where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Applications retaining the SDK across releases and expanding to more platforms can support an adoption hypothesis. Confirm retained integrations and runtime usage with customers. Public reviews do not establish install growth or SDK causality. #### Terms in context - [Edge computing](/glossary#edge-computing) - [Context Buzz](/glossary#context-buzz) - [Friction](/glossary#friction) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [Edge, IoT and hardware](/industries/edge-iot-hardware) - [Cloud and DevOps platforms](/industries/cloud-devops) - [Developer tools and APIs](/industries/developer-tools) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. ### Industries / Agents and AI coding tools (/industries/ai-coding-agents) **Social listening for AI coding tools** For coding-agent and agent-tooling teams. Review observable agent work with the task, repository context and available evidence before drawing conclusions about quality or adoption. Use AI search visibility to understand how answers describe your product and where buyers need better information. *Overview:* EarShots helps agents and ai coding tools teams review public conversations and selected AI search answers with source evidence. #### What your team needs to decide Help product and developer relations distinguish a demonstration, an attempted task and a verified result. Agent activity is useful evidence only within the limits of what the source records. #### Put task evidence behind AI coding-tool recommendations Teams can ask AI which coding agent fits an existing repository and review process. Check whether recommendations distinguish supported tasks from demonstrations and link to evidence of how the tool works. - Read the answer and its cited sources. Check tool versions, task boundaries and documented review requirements with [Ground Truth](/product/accuracy-maxxing), then improve task-specific evaluation guides and integration documentation where the evidence leaves a question unanswered. - Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately. #### What would make the signal useful? Independent teams returning for repeated tasks and retaining the tool in a working process can support an adoption hypothesis. Confirm retained users and accepted outcomes with product data. An agent-authored pull request does not prove a successful task or representative usage. #### Terms in context - [Pull request](/glossary#pull-request) - [Agent Signal](/glossary#agent-signal) - [Context Buzz](/glossary#context-buzz) - [Product-market fit](/glossary#product-market-fit) #### Related industries - [AI and language-model infrastructure](/industries/ai-infrastructure) - [Developer tools and APIs](/industries/developer-tools) - [Testing and quality assurance](/industries/testing-qa) #### Key Questions - **Q: Which sources should we start with?** A: Start with connected public GitHub, Reddit, X, LinkedIn and web sources where your audience discusses the product. Coverage depends on source access and configuration; private conversations are outside this workflow. - **Q: What does AI search visibility tell us?** A: Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search. - **Q: Who reviews and sends a reply?** A: Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies. --- ## Developer GTM Glossary ### Topic: Industry context Technical terms used on the industry pages, with the details needed to interpret a source. #### Inference - **Definition:** Running an AI model on an input to produce an output. - **Usage:** Compare the actual workload, model and operating constraints before interpreting a performance claim. #### Vulnerability - **Definition:** A weakness that could let someone compromise a system or its data. - **Usage:** A public allegation requires investigation; it is not confirmation of a vulnerability. #### Data pipeline - **Definition:** A sequence of steps that moves and prepares data between systems. - **Usage:** Keep the source, destination and failing step attached to an integration report. #### Internal developer platform - **Definition:** Shared tools and workflows that help engineering teams build, deploy and operate software. - **Usage:** Check whether application teams use the workflow, beyond installing a portal. #### Accessibility - **Definition:** Designing a product so people with different abilities and assistive technologies can use it. - **Usage:** Review a specific need, platform and interaction against current feature information. #### Firmware - **Definition:** Software that runs on a hardware device and controls its behavior. - **Usage:** A report needs the device revision and firmware version to be reproducible. #### Remote procedure call - **Definition:** A request to run an operation on another system over a network, often shortened to RPC. - **Usage:** Provider comparisons need the network, operation and request requirements. #### Webhook - **Definition:** An event notification that one service sends to an address configured in another service. - **Usage:** Review the event, delivery and retry rules against the integration documentation. #### Headless commerce - **Definition:** A commerce system whose storefront is built separately from its underlying product and checkout services. - **Usage:** Evaluate the flexibility alongside the integration and operating work required. #### Netcode - **Definition:** The software that coordinates a multiplayer game across a network. - **Usage:** A report needs engine versions, network conditions and the observed behavior. #### Software as a service - **Definition:** Software operated by a provider and accessed as a service, commonly shortened to SaaS. - **Usage:** Review the user workflow, administrative needs and integration requirements separately. #### Single sign-on - **Definition:** A way to access several applications through one identity provider, commonly shortened to SSO. - **Usage:** Keep the identity provider and tenant configuration with a login integration report. #### Deliverability - **Definition:** Whether a message reaches its intended destination, including constraints imposed by receiving systems. - **Usage:** A public complaint cannot establish delivery performance without service evidence. #### Customer experience - **Definition:** How people experience their interactions with a company or product, commonly shortened to CX. - **Usage:** A support-tool discussion may describe an administrator workflow or an end-customer problem. #### Workload - **Definition:** The tasks and operating demands a system must handle, including data size, request patterns and response needs. - **Usage:** Compare tools against a defined workload before interpreting a benchmark. #### Instrumentation - **Definition:** Code and configuration that record what an application is doing through logs, metrics or traces. - **Usage:** Inspect the setup before attributing missing telemetry to the monitoring product. #### Continuous integration - **Definition:** Automatically building and checking code changes as developers combine their work, commonly shortened to CI. - **Usage:** A test that fails only in CI needs the runner, environment and failure details. #### Edge computing - **Definition:** Running application work closer to users or devices rather than only in a central data center. - **Usage:** Read runtime, region and network constraints before comparing performance claims. #### Cross-play - **Definition:** Players on different supported platforms being able to play together. - **Usage:** Check the exact game, platform combination and release documentation before answering. ### Topic: This product Product labels and evidence terms used in EarShots, explained in ordinary language. #### Friction - **Definition:** A reported obstacle to using a product, such as a failed setup step or a missing capability. - **Usage:** Review the source and reproduction details before deciding whether the obstacle is a defect, a documentation gap or a mismatch with the use case. #### Agent Signal - **Definition:** An EarShots view of observable activity involving software agents, with source evidence for review. - **Usage:** Inspect attribution, task context and the available outcome. Agent activity does not by itself establish task success or representative adoption. #### Product-market fit - **Definition:** Evidence that a defined group of customers repeatedly gets enough value from a product to keep using or paying for it. - **Usage:** Use public questions, recommendations and implementation reports to form hypotheses. Validate them with retention, repeat usage, customer research and commercial evidence appropriate to the product. #### Mention - **Definition:** A single post, issue, thread, or article that talks about your product, a competitor, or the problem you solve. - **Usage:** Everything in Insights is a mention. It carries a source, an author, a published date, and a link back to the original. #### Insights - **Definition:** An earlier name for the Mentions queue, where posts are reviewed as questions, friction, buying signals, praise or muted. - **Usage:** It is the first screen after signing in, and the place most of the team works from. #### Context Buzz - **Definition:** A read on how developers actually use your product inside real repositories, and where they get blocked. - **Usage:** It works from GitHub signals only, so the evidence is a repo you can open and check. #### Competitor Signal - **Definition:** Tracking what users say about named competitors: sentiment, buying stage, praise, and complaints. - **Usage:** Every paid plan includes it. Starter tracks 3 rivals, Growth 5, Enterprise 15. #### Ground Truth - **Definition:** A review of product claims against connected documentation and source evidence. - **Usage:** Review the claim, its scope and supporting references. Missing evidence remains unverified; a finding is not proof of a product defect. #### Stats - **Definition:** The measurement surface: volume, sorting outcomes, sources, recurring themes, and team response. - **Usage:** Your performance and Competitor insights use the same boxes, so the two are easy to read side by side. #### Triage - **Definition:** Deciding which lane a mention belongs in and what the next step is. - **Usage:** Jev ranks and labels incoming mentions first. A person confirms or overrides the call. #### Draft - **Definition:** A proposed reply or comment that has not been sent. It is a proposal, never an action. - **Usage:** Nothing leaves the workspace unless a person approves it. There is no auto-post. #### Approval - **Definition:** The human decision that a draft is accurate, on message, and safe to send. - **Usage:** Review happens in the full-screen review view, where the draft, the citations, and the reasoning sit together. #### Citation - **Definition:** A link from a claim in the draft to the document, file, or post that supports it. - **Usage:** A draft without a citation is a guess. Reviewers check citations before approving. #### Trace - **Definition:** The record of how a result was produced: which sources were read, which model ran, and what it returned. - **Usage:** Traces are how you audit a bad answer instead of guessing where it went wrong. #### Source healing - **Definition:** Automatic repair when a connector stops returning results, such as a changed page layout or a blocked request. - **Usage:** Failures are counted and retried, and the source list shows what is degraded rather than silently empty. #### Freshness window - **Definition:** The span of published dates EarShots will pull, currently 180 days. - **Usage:** Older material stays out of the queue, and the original published date is preserved rather than the crawl date. #### Workspace - **Definition:** The account boundary for one team: sources, competitors, members, drafts, and history. - **Usage:** Workspaces can be renamed. Nothing crosses the boundary between two of them. #### Handoff - **Definition:** Passing a mention or draft to another person with the context already attached. - **Usage:** Used when support owns a bug report or sales owns a buying signal, so nobody re-reads the thread from scratch. #### Relevance filter - **Definition:** The scoring pass that drops noise: generic mentions, duplicate posts, low-signal language, and off-topic threads. - **Usage:** It is what keeps a queue readable when the underlying search returns hundreds of results. ### Topic: AEO and GEO How answer engines pick the products and pages they name. #### Answer engine optimization - **Definition:** Answer engine optimization (AEO) is work to make information useful, accessible and accurate for search answers. Mentions and citations can be measured, but are not guaranteed. - **Usage:** Prompt Universe tracks the prompts buyers ask and shows whether your product appears in each answer. #### Generative engine optimization - **Definition:** GEO is the same goal framed around generative search: making your pages easy for a model to retrieve, trust and quote. - **Usage:** Teams use it to plan docs, comparison pages and third-party coverage that models cite. #### Query fan-out - **Definition:** When an AI engine splits one prompt into several related searches and combines the results into a single answer. - **Usage:** Fan-out queries show which sub-questions your content has to answer to be included. #### Cited source - **Definition:** A page an AI answer links to or draws from when it names a product. - **Usage:** Ears Cite contacts the authors of cited sources and asks them to include your product where it fits. ### Topic: The market Category language you will hear from analysts, investors, and the tools on your comparison chart. #### Social listening - **Definition:** Monitoring public conversation about your brand, competitors, and category, then acting on it. - **Usage:** Most listening tools stop at a dashboard. EarShots is built to end at a draft a person approves. #### Developer social listening - **Definition:** Listening where developers talk: GitHub, X, LinkedIn, Reddit, Hacker News, Dev.to, Hashnode, blogs, and docs. - **Usage:** The channels and the vocabulary differ from consumer social, so the query logic and the scoring differ too. #### Developer relations - **Definition:** The function that builds trust and adoption with developers through content, community, and support in public. - **Usage:** DevRel teams are the daily users of a listening queue: they answer, teach, and escalate. #### Developer experience - **Definition:** How good it feels to use your product: install, docs, errors, SDK quality, time to first success. - **Usage:** Complaints about DX surface in public long before they reach a support ticket. #### Signal and noise - **Definition:** Signal is conversation that changes a decision. Noise is volume that does not. - **Usage:** Ranking is the whole product. A queue of everything is a queue of nothing. #### Share of voice - **Definition:** Your slice of the conversation in a category compared with competitors. - **Usage:** Useful as a trend line, weak as a target, because it counts mentions rather than outcomes. #### Buying signal - **Definition:** A public statement that may indicate evaluation or purchase intent, such as a specific request for an alternative. The context still needs review. - **Usage:** In Insights these land in the sales lane with the post attached, not in a lead list. #### Intent data - **Definition:** Third-party signals that a company is researching a category, often from web traffic or firmographic panels. - **Usage:** Conversation intent is different: it names the person, the problem, and the words they used. #### Dark funnel - **Definition:** The parts of a purchase journey that never show up as a tracked touchpoint: threads, word of mouth, private repos. - **Usage:** Public developer conversation is the visible part of that funnel, which is why teams mine it. #### Shadow adoption - **Definition:** People using your product inside a company before procurement or an owner has agreed to it. - **Usage:** It shows up as unprompted questions and setup threads from a single domain or team. #### Product-led growth - **Definition:** A motion where the product itself drives acquisition and expansion, with self-serve signup. - **Usage:** Listening feeds this motion directly: the blockers people describe are the next onboarding fix. #### Bottom-up adoption - **Definition:** Adoption that starts with individual developers and grows upward into a paid team account. - **Usage:** It makes public conversation a leading indicator of a deal, not a lagging one. #### Category creation - **Definition:** Marketing that defines a new problem framing so you are the default answer inside it. - **Usage:** It needs evidence, and listening supplies it: the exact questions people ask before they have a name for the problem. ### Topic: KPIs and metrics The numbers that get reviewed in a QBR, plus an honest note on what each one can and cannot prove. #### Marketing qualified lead - **Definition:** A contact that meets the bar for sales to work, usually a role, company size, and expressed need. - **Usage:** A public post is not automatically an MQL. It becomes one when a person decides the fit is real. #### Sales qualified lead - **Definition:** A lead sales accepts as worth a conversation, after a fit check. - **Usage:** Conversation-sourced leads often skip MQL status entirely, which is why teams track them separately. #### Pipeline influence - **Definition:** Revenue opportunities that a team touched but did not exclusively create. - **Usage:** The honest way to credit listening work: name the deal, name the touch, state the contribution. #### Influenced revenue - **Definition:** Closed revenue traced back to an activity, such as a reply that answered a blocker. - **Usage:** Keep the trace. A number with no trace is a claim. #### Win rate - **Definition:** Share of qualified opportunities that close. - **Usage:** Compare cohorts by where the deal started, so a listening-sourced deal is not judged against inbound form fills. #### Sales cycle length - **Definition:** Time from first qualified contact to close. - **Usage:** Good listening shortens it by removing the objection that was quietly blocking the deal. #### Time to first response - **Definition:** How long between a mention appearing and your team replying. - **Usage:** Reported as a median in Stats, because a single viral thread can wreck the average. #### Median response time - **Definition:** The middle value of response times, so half the replies were faster and half slower. - **Usage:** Use medians for queues. Averages hide the long tail of weekends. #### Deflection rate - **Definition:** Share of questions answered without creating a support ticket. - **Usage:** A good public reply deflects the same question from everyone who reads it later. #### Sentiment score - **Definition:** A summary of the tone of the captured source sample. It describes that sample and may differ from the views of your wider customer base. - **Usage:** Competitor Signal scores sentiment from the user's point of view, and shows the posts behind the number. #### Net revenue retention - **Definition:** Revenue from existing customers this period divided by the same customers last period. - **Usage:** Above 100 percent means expansion outweighed churn. Public complaints are an early warning for the down part. #### Customer acquisition cost - **Definition:** Total sales and marketing spend divided by new customers. - **Usage:** Listening work is cheap per lead, which is why teams use it to bend blended CAC. #### Lifetime value - **Definition:** Estimated gross profit a customer generates over the whole relationship. - **Usage:** Pair it with CAC. A cheap lead that never activates is expensive. #### Activation rate - **Definition:** Share of new users who reach the first moment the product proves its value. - **Usage:** The blockers people post about during setup are the shortest path to a better activation definition. #### Cost per lead - **Definition:** Spend divided by leads produced in a channel or campaign. - **Usage:** Only compare it within a channel. A thread reply and a paid ad rarely produce the same lead. #### Attribution - **Definition:** The model that assigns credit for a conversion across touchpoints. - **Usage:** Most attribution models cannot see a GitHub discussion. That is a gap in the model, not a gap in the influence. ### Topic: Workflows and process How teams run the queue without stepping on each other. #### Queue - **Definition:** The ordered list of work waiting to be handled. - **Usage:** A queue needs an owner and a rank. Without both it becomes a backlog. #### Owner - **Definition:** The single person accountable for the next step on a mention. - **Usage:** Assign it explicitly. Unowned items are the ones that quietly rot. #### Service level agreement - **Definition:** A committed response time for a class of request. - **Usage:** Teams set one for bugs and buying signals, and let everything else run on a longer clock. #### Escalation - **Definition:** Moving an item to someone with more authority or depth when the first responder cannot close it. - **Usage:** A clear escalation path is why a reply lane can include junior staff. #### Runbook - **Definition:** Written steps for a recurring situation, such as a security question or a pricing complaint. - **Usage:** Turn your best replies into runbooks so the answer stops depending on one person. #### Playbook - **Definition:** The agreed approach for a repeatable situation, including tone and what not to promise. - **Usage:** Playbooks are what make a drafted reply sound like your team rather than a model. #### Human in the loop - **Definition:** A design where a person makes the decision that carries risk, and automation only prepares it. - **Usage:** Non-negotiable here: the model drafts, a person sends. #### Closed-loop reporting - **Definition:** Tracking an item from first mention through the outcome it produced. - **Usage:** It is the difference between a team that reads conversation and a team that learns from it. #### Review cycle - **Definition:** The recurring pass where drafts are checked, approved, and released. - **Usage:** Short cycles beat thorough ones that never finish. Most teams run it daily. #### Assignment - **Definition:** Attaching a person to an item so it shows up in their list rather than a shared pile. - **Usage:** Assignment plus handoff covers most of what a ticketing system does for a listening queue. #### Triage cadence - **Definition:** A fixed rhythm for clearing new items, such as twice a day. - **Usage:** Fixed windows beat constant checking, and they make response time reportable. ### Topic: Marketing and GTM jargon The vocabulary of go-to-market meetings, translated. #### Positioning - **Definition:** The choice of problem, audience, and alternative that makes your product the obvious answer. - **Usage:** Listen for the alternative people name when they describe why they left something. That is your real competitor. #### Ideal customer profile - **Definition:** A written description of the customer who gets the most value fastest. - **Usage:** Conversation sharpens it: who asks the questions that turn into activation. #### Messaging house - **Definition:** One document holding the master narrative, pillars, and proof points. - **Usage:** Quote the posts that each pillar claims to answer, or the house is fiction. #### Funnel - **Definition:** The staged model of awareness to purchase. - **Usage:** Useful as a frame, misleading as a description of how developers actually decide. #### Nurture - **Definition:** Ongoing contact that moves someone toward a decision without asking for the deal today. - **Usage:** A helpful public reply is nurture that costs nothing and lasts for years. #### Account-based marketing - **Definition:** Concentrating marketing on a named list of accounts instead of a broad audience. - **Usage:** Listening tells you which accounts are already talking, and about what. #### Go-to-market motion - **Definition:** The repeatable route from unknown to paying customer: self-serve, sales-led, partner-led, or a mix. - **Usage:** Each motion needs a different question from the queue: blockers, budget signals, or integration requests. #### Thought leadership - **Definition:** Content that earns attention by taking a position, not by describing features. - **Usage:** It starts from real arguments people are already having in public. #### Community-led growth - **Definition:** Growth driven by people who use the product helping each other. - **Usage:** Your loudest supporters are visible in the queue before they appear in a CRM. #### Launch - **Definition:** A coordinated public release of something new, timed for attention. - **Usage:** The first 48 hours of conversation after a launch is the fastest feedback you will ever buy. #### Content market fit - **Definition:** The state where a topic reliably earns attention and drives action for your brand. - **Usage:** Tested by results, not by views: does it produce threads, signups, or replies worth having? #### Objection - **Definition:** A stated reason not to buy: price, trust, migration, timing, or an internal constraint. - **Usage:** Public objections are the cheapest research available, and they repeat in the same words. ### Topic: Developer speak The terms that show up in the posts you are reading, so a reply lands correctly. #### Repository - **Definition:** A project's files and history hosted on a platform such as GitHub. - **Usage:** Context Buzz reads repos to see how your SDK is actually imported and configured. #### Issue - **Definition:** A tracked problem or request on a repository. - **Usage:** An issue is a bug report and a public test of how you handle one. #### Pull request - **Definition:** A proposed change to a repository, opened for review. - **Usage:** A contributor's pull request is the highest-intent signal a project can get. #### Discussion - **Definition:** A threaded conversation on a repository, used for questions rather than defects. - **Usage:** Discussions are where the design questions live, and where docs gaps get exposed. #### Star and fork - **Definition:** A bookmark and a copy of a repository. Both indicate interest, not usage. - **Usage:** Treat them as reach. A dependency in a real repo is closer to proof. #### RFC - **Definition:** A Request for Comments: a written proposal open for public critique before work starts. - **Usage:** Watch which RFCs attract strangers with strong opinions. That is unmet demand. #### Breaking change - **Definition:** A release that requires users to change their code. - **Usage:** Announced poorly, it is the fastest way to lose trust. Listening shows you the fallout within hours. #### Changelog - **Definition:** The dated record of what changed in each release. - **Usage:** A good changelog answers the question before the thread gets written. #### SDK - **Definition:** A software development kit: the library and helpers that let developers use your product. - **Usage:** SDK complaints cluster tightly: types, retries, timeouts, and version support. #### API key - **Definition:** A secret string that authenticates calls to an API. - **Usage:** Never ask for one in a public thread, and never include one in a draft reply. #### Self-hosted - **Definition:** Running software on your own infrastructure rather than a vendor's service. - **Usage:** Self-host demand is a compliance and procurement signal, not a feature request. #### Proof of concept - **Definition:** A quick build to test whether something works before committing to it. - **Usage:** A developer stuck mid-POC is the highest-value person in your queue. #### Dependency - **Definition:** A package your project relies on, installed from a registry. - **Usage:** Being a dependency is why a broken upgrade becomes a public event. #### Version drift - **Definition:** The gap between the version someone runs and the current release. - **Usage:** It explains why a documented fix does not work for a specific poster. #### Migration - **Definition:** Moving from one tool, provider, or version to another. - **Usage:** Migration questions are buying signals with a deadline attached. #### Reddit - **Definition:** A network of topic communities where developers compare tools before they buy. - **Usage:** Subreddit threads carry the before-you-buy doubt your docs never see. #### Hacker News - **Definition:** A link-based community where technical products get discussed and judged. - **Usage:** High variance, high stakes: a thread can define a month of perception. #### Docs site - **Definition:** The published documentation for a product, usually generated from a repository. - **Usage:** Ground Truth reads your own docs so a draft says what you actually published.