A visibility percentage can tell you that something changed. It cannot, by itself, tell your team what to do on Monday.
A useful AI visibility report connects three things: the questions you monitored, the answers you collected, and the next action you can justify from that evidence.
I’m Zia, co-founder of Depra AI. We build tools for this workflow, and the reporting structure below is a practical framework you can adapt to your own business.
1. Define the sample before showing the score
Start the report with the monitoring scope: prompt set, language, engines, geography where applicable, dates and collection method. Record changes to that scope.
A chart based on ten comparison questions means something different from a chart based on a hundred general information questions. A score from English prompts does not automatically describe your Hinglish audience. An API response should be labelled accurately rather than presented as every customer’s experience in a consumer application.
Keep a stable core of buyer questions. Use a separate group for experiments so the team can distinguish a change in answers from a change in what you measured.
2. Keep mentions, citations and visits separate
These three signals answer different questions:
- Mentions: did the collected answer name your brand?
- Citations: did it cite your website or another relevant source?
- Visits: did someone arrive on your website, as measured by your analytics?
An answer can recommend a brand without providing a link. It can cite a page without recommending the company behind it. Neither event proves that a customer visited or purchased.
Use labels that make those distinctions visible. Avoid combining them into a single success claim.
3. Show the actual answer
Include a few representative answers with their prompt, engine, collection date and source links. Choose examples that help explain the result rather than only the ones that look flattering.
Your team should be able to inspect an omission, a competitor mention or an inaccurate product statement. If you summarise sentiment, show the language behind the label and check whether the answer correctly describes the product.
Treat collected AI answers as evidence of what the system returned in that sample. Statements inside those answers can themselves be wrong.
4. Compare competitors on the same questions
A competitor comparison becomes useful when the brands are evaluated within the same monitored sample.
Look at where a rival appears and you do not. Then inspect the context. Is the rival relevant to the buyer’s constraints? Does the answer describe your category correctly? Is the source an editorial comparison, a directory, a community discussion or an owned product page?
That investigation may reveal a missing buyer guide, an outdated feature description or a legitimate listing opportunity. It does not establish that copying a competitor’s page will produce the same result.
5. Turn findings into a short action list
For each action, record:
- The buyer question or accuracy problem it addresses.
- The supporting answer or source.
- The specific page, listing or workflow to change.
- An owner and a completion date.
- What you will check after the change.
“Improve AI visibility” is too broad to assign. “Update the pricing page so trial conditions match the current checkout” is concrete. So is “Publish a guide explaining how an Indian marketing team can compare English and Hinglish monitoring results.”
Prioritise changes that help a real buyer understand the product. Earned coverage depends on the publisher’s judgement; a pitch is not a placement.
6. Make the report portable
If your tool provides CSV exports, use them to build a reviewable snapshot in Google Sheets. Preserve the original export, note the collection period, and document any filters or calculations you add.
Importing a CSV does not create a live integration. Make that clear to anyone using the sheet later. Keep customer information and credentials out of shared reports.
A simple workbook can have four tabs: scope, answer evidence, summary and action log. It should let a colleague follow a claim back to the underlying rows.
7. Review changes without promising causation
After publishing a page or correcting a listing, rerun the same core questions over multiple collection periods. Record engine or prompt changes alongside your work.
An increase after an edit is an observation. It does not prove the edit caused the increase: model updates, retrieval changes and response variation can also affect the result.
In Depra AI, teams can inspect tracked prompts, collected answers, source domains, competitor visibility and suggested actions. The product supports ChatGPT, Gemini, Google AI Overviews and Perplexity, with English and Hinglish monitoring. Use the current methodology and plan information at https://depra.ai when interpreting coverage and collection schedules.
The weekly review
End each report by answering three questions: where did we appear, what did the answers say, and what will we change next?
If your team can answer those questions and inspect the supporting evidence, the report is doing its job.