Industries / AI and language-model 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.
A question to track
Which inference platforms fit a team running latency-sensitive applications?
Evidence to inspect
- Saved answer for a selected prompt and engine
- How your product and alternatives are described
- Cited URLs and current product documentation
Use the source gaps to improve workload guides, evaluation methods and current model-limit pages, then recheck the same prompts. Measure customer outcomes separately.
Explore Prompt UniverseThe decision
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.
Answer engine optimization (AEO)
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, 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.
Product-fit signals
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
A little context before you go deeper.
The terms used on this page, with definitions you can return to.
- Inference
- Running an AI model on an input to produce an output.
- Context Buzz
- A read on how developers actually use your product inside real repositories, and where they get blocked.
- Citation
- A link from a claim in the draft to the document, file, or post that supports it.
- Product-market fit
- Evidence that a defined group of customers repeatedly gets enough value from a product to keep using or paying for it.
FAQ
Common questions
Which sources should we start with?
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.
What does AI search visibility tell us?
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.
Who reviews and sends a reply?
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.
Practical guides
Read more about the next step
Choose a guide on reviewing sources, answering questions or measuring AI search visibility.
AI Search & AEO Guides
ChatGPT Search crawler access: a publisher’s guide
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
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
Measure provider-level crawl requests against referred page requests. Compare reporting methods and review crawler access rules for your documentation.
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Connect listening with AI discovery
Bring your product documentation, audience and a decision you need to make. We can review which public sources and AI search questions fit that brief.