Show HN: AI Visibility: Check whether ChatGPT/Claude/Gemini recommend your brand

A Show HN submission for AI Visibility points to https://aivisibility.pro/ and frames the product as a way to check whether ChatGPT, Claude, and Gemini recommend a given brand. The Hacker News thread (item id 49150752) sat at 2 points with 0 comments at the time of this summary, so the launch signal is still early and mostly untested in public discussion.

The product surface, as stated in the title, is a multi-model recommendation check across three major assistants rather than a single-vendor query. That implies a workflow where a brand or product name is evaluated for presence or recommendation behavior in ChatGPT, Claude, and Gemini responses. No architecture, pricing, API surface, or benchmark data appears in the source material, so any claim beyond that multi-model check would be unsupported.

For engineers and builders, the practical angle is discovery: if users ask assistants which tool, library, or product to use, whether those models name your brand becomes a new visibility channel alongside search rankings and social proof. Teams shipping SaaS, developer tools, or consumer products can treat model recommendations as another surface to audit, especially when competitors already appear in answer-style replies and yours does not.

In market terms, ChatGPT, Claude, and Gemini are the three assistants named as the check targets, which matches how many buyers already compare AI answers. AI Visibility sits in the same problem space as brand monitoring and SEO-style audits, but aimed at generative assistants instead of classic search results pages. With only 2 points and no comments on the Show HN thread, competitive positioning and user validation are still unproven on that forum.

Practical next steps: run your own brand through aivisibility.pro, then cross-check the same prompts manually in ChatGPT, Claude, and Gemini so you can see where the tool and live model behavior agree or diverge. Watch the HN thread for early user reports on accuracy, false positives, and which query patterns the check actually covers. If the tool is useful, fold periodic brand-recommendation checks into launch and positioning work the same way you would review search snippets or comparison-page coverage.