// Brand answers
Own what the machines say about you
Wrong fees. Dead products. Stale facts, served to every customer who asks. We find each one, trace it to the page that taught it, and correct the record at source.
A glitch, repeated forever
A support page you retired in 2023 is still teaching a model your old fee structure. An explainer written by a competitor is still the most cited source about your product. Nobody on your team has read either one in years, and both are being recited to buyers every day, in a channel with no impressions report and no complaints inbox.
That is the problem this service exists for. Not visibility. Accuracy.
What AI gets wrong about crypto brands
The errors are not random and they are not the ones a generalist agency goes looking for. Run enough brand questions through enough engines in this category and the same five keep coming back.
Repeated FUD. An exploit, a depeg, a chain halt, a lawsuit or a bank run gets compressed into the one sentence a model reaches for whenever your name comes up. Years of remediation are invisible to it. The worst month you ever had is still the most written about thing about you, so it is still the most retrievable.
Stale listing and delisting information. A model recommends an asset you delisted eighteen months ago, or tells a user in a market you exited that they can sign up today. Both answers were correct when the page that taught them was written.
Outdated fee schedules. Maker and taker figures, withdrawal costs and spreads quoted from a pricing page you replaced, or worse, from a comparison article that was never accurate and now outranks you on the question.
Token and project name confusion. Two tokens sharing a ticker. A fork treated as the original. A rebrand merged with the thing it replaced. A protocol and its foundation described as one entity, or a v2 fact attached to v3. Models resolve near-identical names by picking one, and there is no reason it will be yours.
Wrong licensing and jurisdiction claims. The most dangerous of the five. An engine states you are regulated somewhere you are not, or that you are unavailable in a market you are licensed in. Nobody at your company said it and every prospective customer who asks is being told it.
This is where the framing has to change. None of those are marketing bugs. A misquoted fee is a disclosure problem. An invented licensing status is a statement about your regulatory standing, made at scale, to prospects, in a channel with no impressions report and no complaints inbox. Your compliance team reviews every word on your own site and cannot see a single one of these.
We have measured the pattern on a public subject rather than asserting it. In our own scan of Uniswap, a protocol we use as a test case and not a client, the findings clustered on incident history, token supply, governance and tokenomics, which are precisely the fields where being wrong carries weight in this industry. Those are also the fields nobody has written a fresh, authoritative, first-party page about since the last funding round.
How we measure it
We run a fixed set of questions about your brand through every major engine, several times each. Running once is guesswork: engines are non-deterministic, and in our own testing seven of eighteen contradictions appeared in only one run out of three. A tool that asks once would have sold all seven as findings. We separate what repeats from what does not, and we report the difference.
Every claim the engines make is checked against your fact sheet, a document of verified ground truth you sign off on before anything is measured. Building that document is usually the first real deliverable, because most teams have never had one.
A wrong answer is a symptom, not a typo
The instinct is to treat a false claim as an error to be corrected, one line at a time, forever. That is a losing game, because the engine did not make a mistake. It reached a conclusion from what it could find, and what it could find was weighted against you.
So we do not patch wrong answers. We diagnose the authority gap that produced them and rebuild the signal at the source. When a model states your fee structure wrong, the real finding is that the stale version of that fact was easier to retrieve than the current one, and that is a fixable condition rather than a recurring chore. Fix the condition and the whole cluster of related errors goes with it.
The part nobody else does
Finding a wrong answer is easy. Finding out why is the work. Engines attach their sources to their own answers, so for each false claim we can name the exact pages cited alongside it. That turns a vague complaint into a list of URLs.
In our own test scan, seven of the wrong claims traced back to pages the brand itself controlled. Those are not lobbying problems. They are afternoon problems.
What this does not do yet
We report what the engines say now, and what changed between one measurement and the next once you have more than one. We are honest that continuous drift alerting is still being built rather than shipped, and we would rather tell you that than sell you a dashboard that does not exist. What you get today is measurement, tracing, and a fix list, reviewed by a person before it reaches you.
Straight answers.
AI says something wrong about us. Can you fix it?
Usually, and not by patching the answer. Nobody can edit a model, and anyone promising they can is selling something they cannot deliver. What we do is find the pages the engine cited when it got you wrong and rebuild the signal there. A wrong answer is rarely a typo. It is what an engine concludes when the stale version of a fact is easier to retrieve than the current one.
How do you know what is wrong and what is right?
A fact sheet you sign off on before anything is measured: verified ground truth, every fact carrying the primary source it came from. This is the unglamorous part and it is the part that decides whether the whole engagement is worth anything. A wrong fact sheet produces confidently wrong findings forever, in a document with our name on it, so we would rather spend a week building it properly than start scanning on day one.
Do you score our brand for trust or accuracy?
No, and we will not build one. A trust score derived from AI output is a number with no defensible method behind it, and the moment it appears in a deck somebody treats it as fact. Every finding here is what a named engine said, on a named date, with the sources it cited. That is auditable. A score is not.
Why does a person review the findings?
Because the automated pass produces false positives, and we would rather tell you that than pretend otherwise. In our own testing at least one finding in eleven was a disagreement that turned out not to be one. Software gets you a triage queue; a person gets you a finding list. The review is most of what you are paying for, and it is the reason this is a service rather than a dashboard subscription.
Why does this matter more in crypto?
Because the fields engines get wrong most often are the ones that carry legal weight here. In our scans, the errors cluster on incident history, token supply, governance and tokenomics, along with fee schedules, delisted assets and invented licensing claims. Misstate a hotel check-in time and somebody is annoyed. Misstate a custody model to every prospective customer who asks, and you have a compliance exposure nobody on your team can see.
See what the engines see.
Run the free scan and see your site the way the machines see it. No login. When you want the full picture, the Audit takes three weeks.