AI Visibility for Fintech: when AI misquotes your fees, that's risk
In a trust-critical category, what the engines say about your rates, charges and compliance status has to be accurate before it gets to be flattering.
Customers ask assistants “is this app safe,” “what does it charge for international transfers,” “is it regulated.” Engines answer with full confidence whether or not they're right, a hallucinated fee table or an outdated compliance claim reads as authoritative as the truth. In a YMYL category, that's risk surfacing in a channel nobody in the company owns.
Engines also hold financial content to stricter scrutiny than most categories. Accuracy, authority signals and structured facts decide whether they cite you directly, or cite a generic aggregator's secondhand summary of you.
The dashboard, in your category's terms
Shipped with your team, verified live
We ship corrections: authoritative fact pages, schema, an llms.txt that states what's true, structured FAQ for the exact questions engines fumble, each one verified live before it counts.
How we measure
The proof behind this page
When we measured our own program, we ran it against a control brand and published the placebo test: organic sessions track branded demand at r = 0.85, while direct traffic, which shouldn't correlate, comes in at r = 0.04. The control brand's branded clicks declined 14% over the same window.
Fintech & Financial Services, by vertical
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