AI Visibility Reporting for Agencies: How to Add AI Search to Your Service

Why are clients asking about AI visibility?

Because they have seen what AI assistants say about them, or about their competitors, and they don't know whether it's good or bad. Clients want someone to measure it, explain it and improve it, and they expect their search or content agency to have an answer.

The question usually arrives informally. A marketing director asks ChatGPT about their category and their brand isn't named. A CEO forwards a screenshot from Perplexity with a competitor at the top. A board member asks whether "AI search" is part of the plan. Suddenly the agency is expected to have an opinion.

This is an opportunity, not a threat. Agencies already understand search, content, digital PR and reputation, which are exactly the levers that influence AI answers. What is often missing is the measurement layer: a reliable way to show what AI assistants say, across which questions, compared with which competitors, and how it changes over time.

There is also a commercial reason to move early. Clients increasingly see AI search as a separate line item from classic SEO. Agencies that can report on it credibly can expand existing retainers, open conversations with new clients and defend their SEO work by showing how it carries into AI answers. Agencies that can't risk being seen as working on yesterday's channel.

The work itself is not exotic. It is generative engine optimization (GEO) and answer engine optimization (AEO): understanding which sources AI assistants rely on and improving how a brand is represented in them. The new part is doing it with data instead of screenshots.

What should an AI visibility report include?

At minimum: visibility across a fixed set of buyer questions, share of voice against named competitors, sentiment, the sources AI assistants cite, and the actions taken since the last report. Keep it short enough that a client reads it.

A useful structure for a monthly report:

• Headline visibility. The share of AI answers to the tracked questions that mention the client, with the change since last period.
• Share of voice. How the client compares with three to five named competitors across the same answers.
• By assistant. A breakdown across ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews and Mistral, because the picture often differs.
• Sentiment and description. How the assistants describe the client, with real example answers.
• Sources. The domains and pages cited most often, and which of the client's own pages appear.
• Actions and results. What was done since the last report, and what moved afterwards.
• Next steps. The few actions planned for the coming period.

Use real answers as evidence. A quoted paragraph from an assistant is more persuasive than any chart, and it keeps the conversation grounded. Our GEO reporting templates include a client-ready structure you can adapt.

Avoid made-up composite scores that you can't explain. Clients trust numbers they understand: share of answers, share of voice, and a clear before-and-after.

How do you track many client brands at once?

Give each client its own workspace with its own questions, competitors and markets, and run tracking on a schedule so reports build themselves. Manual checks don't scale past one or two clients.

The operational challenge for agencies is volume. Each client needs its own set of questions, its own competitor list and often its own markets and languages. Multiply that by a client roster and by six AI assistants, and manual checking becomes impossible.

A workable setup looks like this:

• One workspace per client. Separate data, separate competitors, separate settings, so nothing leaks between accounts.
• A standard question framework. Use the same four question types for every client (category, problem, comparison and alternatives), adapted to their market. It makes onboarding faster and reports comparable.
• Scheduled runs. Tracking runs automatically, so the data is ready when the report is due.
• Exports for reporting. Data you can pull into your own report format or client dashboards.
• Team access. Account managers and specialists working in the same accounts without sharing passwords.

Refora is built to work this way, with multiple workspaces under one organization. You can see how agencies use it on the Refora for agencies page. Some teams also connect Refora to their own AI assistant through the MCP server, so they can ask questions about a client's data in plain language while preparing a report.

How do you package AI search work for clients?

Package it as a clear loop the client can understand: an initial AI visibility audit, ongoing monthly tracking and reporting, and a set of improvement actions each month. Price it like any other ongoing search service, based on scope.

Three common packages:

1. AI visibility audit. A one-off project: define the questions, run them across assistants, benchmark competitors, map the sources and deliver a prioritized action list. This is the easiest entry point and often leads to ongoing work.

2. Monitoring and reporting. Monthly tracking with a short report and a review call. This suits clients who want to keep an eye on the channel while you work on other priorities.

3. Monitoring plus improvement. Tracking, reporting and a defined number of actions per month: new or improved pages, source outreach, digital PR, profile updates. This is where most of the value is, because it closes the loop between data and results.

Whatever the package, set expectations honestly. AI answers vary, and some changes take time to show up. Promise a process and transparent measurement, not guaranteed positions. Clients respect an agency that shows the real answers, explains what moved and admits what didn't.

Finally, connect AI search to the work you already do. Strong SEO, good content and real PR all feed AI visibility. Showing that connection makes the new service an extension of your existing value, not a separate experiment.

Frequently asked questions