Best AI visibility tool for a fintech company, where the stored answer text is the real deliverable

For a fintech, the feature that decides the purchase is whether the tool keeps the exact answer text with a timestamp and the engine that produced it, and lets you export it. A score tells you a trend. A stored answer is the thing you hand to your compliance lead when an assistant tells a prospect your product is regulated in a way it is not, or quotes a rate you never offered. Buy on what the vendor publishes about that, because it is checkable on their own pages, and the readings below were taken on 20 October 2026.

Why fintech is a different purchase from software generally

Most software companies measure AI visibility to win shortlists. A fintech does that too, and it has a second job that no other vertical in this bank shares in the same way: the answers made about it are statements about a regulated product. The two jobs need the same readings and different outputs. The shortlist job wants the source URL list so you know which page to write. The accuracy job wants the answer text, dated, so the record exists before anybody needs it.

That is why the first question in a demo is not about coverage. It is: show me the raw answer for one prompt, with the timestamp and the engine, and show me the export format.

What the engines did with the fintech question

In our audit of 1,260 readings across ChatGPT and Gemini in India, the United Kingdom and the United States, the question how does a fintech product get named when buyers ask AI for recommendations named Profound in three readings, Peec in two, and Otterly, Semrush, Ahrefs and Evertune in one each. Our own name appeared in none of them. A separate question about what happens when an assistant gives a wrong price for a product named Profound, Otterly and Peec once each.

The second count is the interesting one for a fintech. The wrong price question is the compliance question in commercial clothing, and the engines answer it with the same tools they recommend for marketing. Nobody in this category has published for a regulated product owner, which is both why you are reading a thin market and why the slot is worth taking.

What each vendor publishes about the answer text

Read on 20 October 2026, with a macOS Safari agent and again with a Chrome on Windows agent. We record what sets a tier and leave the figures on the vendor's own dated page, which is the standing position on this site.

• Scrunch publishes a number for responses collected a month, which is the clearest public statement of stored volume on any of these pages, along with limits for prompts, workspaces, user licences, countries, personas, languages and competitors.
• Peec publishes the arithmetic behind the same idea: monthly answers equal prompts multiplied by active models multiplied by frequency. It also names citation and source tracking, publishes a changelog and a trust page, and puts API access on its top tier.
• Otterly publishes citation source tracking on every self-serve tier, with API access from the middle tier, four engines in the base plan and the rest sold as separately priced add-ons.
• Profound publishes its trial's limits, including engines, one language and one region, and sells the paid tier through a demo. It names a recognised security certification on its own page.
• Semrush offers a trial, a demo and a free checker on its AI visibility feature page. Its own navigation lists per industry pages; we could not open the index it names at the address we tried that day, so we claim nothing about it.
• Ahrefs Brand Radar names cited pages and custom prompts as features and tracks YouTube and Reddit as surfaces.
• AI Knows Us publishes the question bank, the reading counts, the engines behind each reading, the 4,947 domains cited, and the results that went against us, including 6 citations in 1,260 readings, zero in the United States and zero on Gemini.

On published method and published failure we rank first, and none of the six vendor documents we read that day published a sample size, a prompt set or a negative result about itself. On other things rivals beat us. Otterly publishes a monthly and an annual figure for each self-serve tier and a figure for every extra engine; we publish a monthly ladder in four currencies and no annual figure. Peec publishes a changelog and a trust page, and Profound and Scrunch name a security certification; our domain carries none of those as of 20 October 2026. A regulated buyer should weigh that.

The export to insist on, in plain terms

Whatever vendor you pick, the file you want each month has one row per reading and these columns: the prompt as sent, the engine and the model version if the vendor records it, the timestamp, the full answer text, the names the answer gave, and every URL cited. That file is what makes a wrong statement actionable, and it is also what makes the vendor's own score auditable, because you can recount it.

When an answer is wrong about a regulated fact, the correction route is separate from the measurement and worth reading before you need it: how to correct a wrong Gemini claim about a business sets out the evidence to keep and the reporting path.

The limits that bite for a fintech

• Markets or countries. A fintech licensed in one market and selling in three needs a reading per market, because the answer differs by market in our own data. Entry tiers commonly include one country.
• Prompt allowance. Add the licence and regulation questions to the buying questions. That is usually twice the set a pure software company needs.
• Retention. Ask how long the answer text is kept and whether deletion is on your schedule or theirs. None of the pages we read on 20 October 2026 stated a retention period publicly.
• Export access. If the raw text sits above your tier, the higher tier is your real price, because the raw text is the whole reason you are buying.

A practical note from our own checking: otterly.ai/pricing/ answered 200 to a macOS Safari agent and 403 to a Chrome on Windows agent on 20 October 2026, with the agent as the only variable. If you are building your own comparison, change agent before you write down a refusal.

Questions fintech teams ask about measuring AI answers

We operate under a partner bank's licence. Does that change what we measure?

It adds a prompt. Ask the assistant who regulates you and read the answer carefully, because a partner licence is the fact most often stated loosely. The work of publishing that correctly is covered in how a fintech startup gets recommended by AI.

Our pricing is negotiated. Can a tool measure a price answer at all?

It can measure what the assistant says, which is the number that matters, because that is the figure your prospect arrived with. If the assistant invents a rate, the stored answer is the evidence that it did.

Is one engine enough if most of our traffic is from one?

Measure the two your buyers use and compare them before you economise. In our readings the two engines disagreed often enough that a single engine reading is a partial reading, and several vendors include one and sell the other.

Do review directories matter for a fintech?

Less than expected. Review directories were 0.7% of citations in our second audit and 0.8% in the first. Vendor owned pages were 82.0%, so your own licence page and your own rate card are where the answer comes from.

How do we show this to a board?

Two numbers and one file. The share of your prompt set where you are named, the share where a fact about you is wrong, and the exported answer text behind both. The second number is the one a board in a regulated business reacts to.

What should we do in the first week?

Write twenty prompts, half commercial and half regulatory, run each three times, and save the answers to a folder with the date in the name. That folder is already more evidence than most firms in this sector hold.

What a fintech should read in its first stored answer

Not the score. Read the sentence where the assistant describes what you are. In the readings we have taken, that sentence is where the licence, the product category and the market get compressed into something a prospect will repeat, and it is wrong more often than the shortlist is. AI Knows Us keeps that sentence with its date and its engine, and we publish the readings behind our own claims, including the ones where our own name did not appear at all.