How can I see whether ChatGPT and other AI assistants recommend my fintech company?

The short answer

Ask the assistants the questions your buyers ask, the way they ask them, and record which companies get named and how each one is described; then ask the same questions again regularly to see what changes. SkyParlour runs benchmarking audits as part of our AI and GEO services, showing a fintech company where it stands today in AI-generated answers.

Run a simple check yourself

Write the buyer questions

Ten or so questions a buyer would ask before shortlisting a company like yours, in their words rather than yours.

Ask more than one assistant

ChatGPT, Claude, Gemini and Perplexity, with web search on and off, because each draws on different sources.

Record the answers

Who is named, in what order, how each is described, and which sources are cited.

Repeat on a schedule

The same questions every month, so you can see whether you are named more often and described more accurately.

Ask the questions your buyers ask. Count who gets named.

What the results tell you

If you are not named, the assistants either cannot find clear information about you or trust other sources more. If you are named but described wrongly, something out of date or unclear is being repeated.

If others are named and you are not, look at the sources the assistants cite. Those are the places your business needs to be.

What a benchmarking audit adds

A one-off check shows a single moment, and answers vary from day to day. A benchmark asks the same questions of the same assistants over time, so a change can be measured rather than guessed. Benchmarking audits are part of our AI and GEO services.

Questions people also ask

Why do different assistants give different answers?

They draw on different sources, and some answer from what they learned in training while others search the web first. That is why it is worth checking more than one.

How often should we check?

Monthly is a sensible rhythm. What an assistant finds with web search can move within weeks; what a model knows from training changes only when it is retrained.