Buyers used to start at a search box and scan ten blue links. A fast-growing share of them now ask ChatGPT, Perplexity, or Gemini 'what is the best X for someone like me' and act on the one answer they get back. A decade of SEO tells a brand how it ranks on a results page. None of it tells you whether an AI assistant names you at all, how it describes you, or where it picked up that opinion.
That answer is also hard to even see. It shifts with the phrasing, the persona, the language, and the engine, so checking once proves nothing. The only honest way to know where a brand stands is to measure a distribution of answers, repeatedly, the way real buyers actually meet them. The few tools that do this are built for enterprise budgets and a Western source graph; none of them measure the messy, multi-language reality of the buyers this platform serves.
A tool that shows a brand whether AI assistants actually recommend it, and what to do when they don't. More and more people don't Google a product anymore; they ask ChatGPT, Perplexity, or Gemini 'what's the best one for me?' and trust whatever comes back. Most brands have no idea whether the AI even mentions them, how it describes them, or where it got that opinion. This measures exactly that across five different AI assistants, by reading the real answers a buyer would actually see, not a stripped-down version through a back door. It works out where a brand is invisible and why, then hands back a ranked, ready-to-run plan to fix it. Every brand using it sees only its own category and competitors. I built all of it solo, end to end: the part that captures the answers, the scoring, the recommendations, and the app itself.
Measure the real surface, not an API
What matters is the answer a person actually sees, which is not what a model returns through a paid API. So the platform captures from the live consumer surfaces themselves and persists the raw answer, its citations, and a screenshot as an audit trail before anything is parsed. The hard part was never the reading. It was doing it safely, repeatably, and without tripping the systems that guard those surfaces.
Honest metrics or none
Visibility is a distribution, not a single number. The metrics deliberately drop the easy wins (questions that already hand over the brand name), separate a model politely declining from a genuine absence, and ship every figure with the sample size behind it. When something cannot be computed cleanly, the product says 'insufficient data' instead of inventing a number.
Strategy in front, evidence behind
Recommendations are generated from the data first, off the actual gaps in the captured answers, then written up against the specific evidence that triggered each one. The strategies are grounded in published research on what genuinely moves AI answers, but a client only ever sees a clean play and a template, never the machinery underneath.
Boring infrastructure, on purpose
Multi-tenant the simple way: every record partitioned by tenant, a read model the app serves fast, no speculative agent frameworks or vector databases. The paid models run only over text already captured, never on live web calls, so the platform stays cheap to operate. The novelty is the data and the judgment; the plumbing should be unremarkable.
What the user sees beats what the API says
Reading the live consumer surface is slower and more fragile than calling an API, and it has to be paced carefully. It is worth it, because the API answer and the answer a real buyer sees are not the same thing. The limitation is written into the methodology, not hidden from it.
Awareness, never revenue
The headline metric is brand awareness for the AI era: a leading indicator, not an equals sign to sales. Refusing the easy causal-revenue claim is exactly what makes the rest of the numbers believable to a sharp buyer.
Research-grounded, research-hidden
Every strategy traces internally to evidence and a published finding, yet none of that reaches the client. Credibility is built into the engine; the methodology stays proprietary. The product sells a clean decision, not a reading list.
Multi-tenant from the first line
Tenancy was not bolted on. The very first data model partitioned everything by tenant, and isolation was verified before any feature shipped. For a platform whose whole promise is competitive intelligence, one tenant glimpsing another's data is not a bug, it is the end of the product.