Find. Fix. Defend.

We put the questions your buyers actually ask to ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews, and record what comes back: whether you are named at all, where you rank, which competitors get named instead, and whether what the model says about you is true.

Every answer is stored, so the finding is evidence rather than an opinion. If too few engine calls succeed to support a defensible number, we withhold the score and say so instead of publishing one built on thin data.

The headline score starts at 100 and loses points for the shopper queries you are invisible for. Alongside it we report two numbers that measure different things and do not feed into it: visibility, how often models surface you, and technical readiness, whether your site publishes the structured signals a machine needs to verify you.

Splitting them matters, because they fail independently. A site can be technically perfect and still be invisible — that is the most common result we see, and it is our own result too.

We ship the corrections: structured data a machine can read, content that answers the questions models are being asked, consistent facts across every surface that describes you, and citations from sources a model already trusts.

Fixes are ranked by the score impact of resolving them, so the first thing you do is the thing that moves the number most.

Models are retrained, competitors publish, and an answer that named you in March can drop you by June. We re-audit monthly and hold the position, and you can see the score history rather than taking our word for the trend.

Measurable improvement in 30 days, or your money back.

The audit is free to start and runs the same pipeline described above — not a demo of it. Run a real audit, or read the frequently asked questions.