The problem this solves
Different engines disagree about your category, and the disagreements are the intelligence. An answer that names you on one engine and omits you on another is telling you exactly where the next citation campaign should aim - but only if you measure both sides with the same questions.
GPT-based answers reach more buyers than any other engine family, and they lean on a particular diet of sources. Knowing which domains those answers trust for your category is knowing where a mention moves the needle - and it is rarely the same list that wins Google.
Under your own OpenAI key, the check runs at whatever depth your prompt set needs, on your account, with your cost ceiling - a measurement program rather than a favor from someone else's quota.
The split verdicts matter more than the averages. A category where every engine agrees is expensive to move; one where GPT omits you while two other engines recommend you is a gap with a discoverable cause - usually one missing source in the diet those answers trust. Finding those asymmetries systematically, rather than through a colleague's alarming screenshot, is the difference between running a citation strategy and reacting to anecdotes.
How the mission runs
- Run the buying-intent prompt set through GPT. The agent asks GPT the questions your customers actually ask - your tracked set, verbatim - through your connected OpenAI key, capturing full answers rather than summaries.
- Score your presence per answer. Each answer is scored: named or absent, recommended or merely mentioned, and which competitors appear ahead of you. The scoring rubric is identical to your other engines so the numbers compare.
- Trace the source diet. Where answers reference sources, the agent captures and aggregates them; where they do not, it probes what the answers rely on with follow-up checks against the live web.
- Compare side by side with your tracked engines. The GPT results are laid against your existing engine tracking in one table: where you win everywhere, where you lose everywhere, and where the engines split - the splits being your cheapest wins.
- Deliver the verdict. A short report lands with the side-by-side, the source-diet analysis, and the two or three moves most likely to change what GPT says about you.
The prompt
This is the exact objective the agent receives. Swap the obvious placeholders for your own domain, segment or channel and run it as-is from the console, Slack, or the API.
What comes back
A GPT visibility report on your exact prompt set: presence scores, competitor positions, source-diet analysis, and a side-by-side against your tracked engines.
Make it yours
- Run it as a standing monthly check so engine drift shows up as a trend, not a surprise.
- Restrict to one product line and its five hardest competitive prompts for a deep cut.
- Add a rewrite pass: have the content agent draft the page most likely to earn the missing citation.
- Turn the split verdicts into a quarterly experiment: pick the one prompt where GPT omits you, publish the page its source diet suggests, and re-run the check monthly to watch whether the answer moves - visibility work with a control group.
Frequently asked questions
Why measure GPT separately from ChatGPT tracking?
Model families and retrieval behavior differ across surfaces and versions. Under your key you pin the model and depth, which turns anecdotes into comparable measurements.
Is my OpenAI key used for anything else?
No - connections are scoped to the workspace and the key is used for the missions you run. Disconnect any time under Settings.
What if GPT never cites sources for my prompts?
That is a finding in itself: the agent then probes what the answers correlate with on the live web and reports the likely diet, honestly labeled as inference.