When people look for software today, a growing share of them never touch Google. They ask ChatGPT, Claude, Perplexity or Gemini "what's the best tool for X?" and take the answer at face value. If your product isn't in that answer, you're invisible to those users, and you have no idea it's happening.
I kept running into this with our own products, so I built AskAiRank to measure it. This post is about the problem and how the tracking actually works under the hood.
Classic SEO gives you rank trackers, Search Console, backlinks. For AI assistants there's almost nothing. You can't see:
And the answers aren't deterministic. Ask the same question twice and you can get different tools, different ordering, different citations. So a single manual check tells you nothing, you need to sample over time.
The core loop is simple:
The parsing is the hard part. Model output is prose, not JSON, so mention detection has to handle aliases, partial names, "also worth checking out X" asides, and citations that live in footnotes or inline links. It's the piece I'm still iterating on the most.
Nothing exotic:
People are starting to call this AEO (Answer Engine Optimization) or GEO (Generative Engine Optimization). The name matters less than the shift: recommendation is moving from ranked blue links to a single synthesized answer. If you ship a product, it's worth knowing where you stand in that answer, the same way you'd check your Google rank.
If you want to try it on your own product, there's a free plan (no credit card): https://askairank.com I'd genuinely love feedback, especially from anyone who has tackled mention-detection in free-form LLM output. Where does your approach break?