The Monthly AI Visibility Report Your Clients Should Be Getting (Free Template)

If you sell AI visibility monitoring to clients, the monthly report is not a byproduct of the service. It is the service. The scans run in the background; the report is the thing the client actually opens, forwards to their business partner, and weighs when the renewal conversation comes around. A retainer with mediocre reporting churns even when the underlying work is good, because the client cannot see the work.

Most agencies improvising AEO reporting today send one of two things: a screenshot of ChatGPT with the client's name highlighted, or a raw data export nobody reads. Neither survives contact with a business owner. This article walks through each section a client-ready AI visibility report needs, in order, with a copyable outline at the end you can adapt into your own template.

Three principles before the sections

Lead with one number. A business owner will give the first page five seconds. That page should carry a single visibility score and its direction of travel. Everything else in the report exists to explain that number.

Numbers over adjectives. "Visibility improved nicely" is filler. "Appeared in 11 of 25 tracked answers, up from 7 last month" is a report. If a sentence in your report contains no number and no action, cut it.

Every metric pairs with an action. Measurement without a fix list is theater — a criticism market reviewers have leveled at the AI-visibility tool category generally. The report's job is to end in work: what you did, what you will do next, and why.

Section-by-section: what a great report contains

1. Visibility score and trend

Open with a single 0–100 score summarizing how visible the client is across the tracked prompt set and engines, charted against at least the previous three months. The methodology matters less than the consistency: define how the score is computed (mention frequency, position in the answer, engines weighted equally or not), write it down once in an appendix, and never change it silently. A score whose method quietly shifts is worse than no score, because the trend line stops meaning anything.

Under the chart, add two sentences of plain interpretation: what moved the score, in language a non-marketer understands. "The score rose 6 points, driven mainly by new mentions on Perplexity after the March review push" is the register to aim for.

2. Mention rate by engine

One table. Rows are engines — ChatGPT, Claude, Perplexity, Gemini — and columns are: answers containing the client this month, last month, and the tracked prompt count.

(The numbers above are illustrative, not benchmarks.) Breaking the data out per engine does two things. It shows the client that "AI" is not one thing — engines differ in what they cite and whom they name — and it explains volatility honestly: a flat month on one engine alongside gains on another reads as measurement, not spin.

3. Competitor share of voice

For every tracked prompt, someone is being named. Share of voice shows who. List the top five to eight businesses appearing across the prompt set, with the percentage of answers naming each, client included, this month versus last.

This is consistently the section clients care about most, because it converts an abstract score into a rivalry they already feel. It also sets up the fix list: if a competitor holds 40% share of voice and the engines keep citing one review platform when naming them, the action item writes itself.

4. Cited sources

Engines assemble answers from somewhere: review platforms, directories, industry publications, Reddit threads, the client's own site. Report the domains most frequently cited in answers across the client's prompt set, split two ways — sources cited in answers that mention the client, and sources cited in answers that do not.

The second list is the strategy. Those are the AI-trusted sources where the client is currently absent, and building presence there (a profile, a review base, a mention, a listing) is among the highest-leverage AEO work available. This section turns "get more citations" from generic advice into a named target list.

5. Sentiment and description accuracy

Being mentioned is half the battle; being described correctly is the other half. Record how engines characterize the client when they do appear: positive, neutral, or negative framing, and — often more important — factual accuracy. Engines confidently repeat outdated service areas, wrong specialties, and stale pricing. A line item like "Gemini describes the client as residential-only; commercial services launched in January" is a concrete, fixable finding, and exactly the kind of detail that makes a client feel the monitoring is worth paying for.

Keep this section short: a sentiment tally plus any specific misstatements found, each with the planned correction.

6. The prioritized fix list

The closing section, and the one that justifies next month's invoice. Rank the top three to five actions by expected impact, and tie each to evidence from the sections above. The recurring categories:

• Schema markup. Missing or thin structured data — Organization, LocalBusiness, Product, FAQ — that helps engines parse who the client is and what they do.
• llms.txt. A machine-readable summary file for AI crawlers; quick to ship and increasingly checked. Also confirm AI crawlers are not blocked in robots.txt.
• Reviews. Volume, recency, and rating on the platforms the engines actually cite for this category (Section 4 tells you which).
• Citations on AI-trusted sources. Presence on the specific directories, publications, and communities appearing in the cited-sources list where the client is absent.
• Entity presence. Consistent name/address/phone across the web, knowledge-graph presence, correct category associations — so engines can resolve the client as a distinct entity rather than hedging.
• Content gaps. Tracked prompts where no page on the client's site answers the question being asked. Each is a content brief.

For each item: what, why (evidence), who does it (agency or client), and status of last month's items. Carrying forward the prior month's list with statuses — shipped, in progress, blocked — is what makes the report cumulative instead of episodic.

The copyable outline

AI VISIBILITY REPORT — [Client] — [Month Year]
Prepared by [Agency]

1. Summary
   - AI Visibility Score: [n]/100 ([+/-n] vs last month)
   - Trend chart (4+ months)
   - Two-sentence plain-language interpretation

2. Mention rate by engine
   - Table: ChatGPT / Claude / Perplexity / Gemini
   - Answers mentioning client, this month vs last, prompts tracked
   - One note on any engine-level shift

3. Competitor share of voice
   - Top 5–8 names across tracked prompts, % of answers, vs last month
   - One-line takeaway: who gained, who lost

4. Cited sources
   - Domains engines cite most for this prompt set
   - Split: sources behind client mentions vs sources behind
     competitor-only answers
   - Target list: where the client is absent

5. Sentiment & accuracy
   - Positive / neutral / negative tally when mentioned
   - Specific misstatements found + planned correction

6. Prioritized fix list
   - Top 3–5 actions (schema, llms.txt, reviews, citations,
     entity presence, content gaps), each with evidence and owner
   - Status of last month's items

Appendix
   - Prompt set (20–30 buyer-intent questions)
   - Scoring methodology (fixed; changes announced, never silent)
   - Note on answer volatility: single runs vary; trends are
     reported across weekly scans

Delivery notes

Send it the same week every month — consistency is half of perceived professionalism. Scan weekly but report monthly, so the client sees trend rather than noise; the weekly data is for you, the monthly synthesis is for them. Keep the whole document under six pages. And brand it as yours: your logo, your colors, your domain on the share link. The client is paying your agency, not your software vendor, and the report should say so.

A note on volatility, because it belongs in every report's appendix: AI answers are non-deterministic. The same prompt can return different shortlists on different days. That is not a flaw in the measurement — it is the reason the measurement exists, and the reason it is a subscription rather than a one-time audit. State it plainly and clients handle dips like adults.

If you would rather not build and populate this template by hand every month, this is the product we built. MentionScope runs weekly scans across ChatGPT, Claude, Perplexity, and Gemini for each client, computes the score, tracks share of voice and cited sources, and generates this report white-labeled with your logo — for all your clients at one flat price. You can also run a single free scan of any domain with the AI Visibility Checker to see the data the report is built from.

FAQ

What should a monthly AI visibility report include?

Six sections: an overall visibility score with trend, mention rate broken out by engine (ChatGPT, Claude, Perplexity, Gemini), competitor share of voice, the sources engines cite, sentiment and description accuracy, and a prioritized fix list covering schema, llms.txt, reviews, citations, entity presence, and content gaps.

How often should you measure AI visibility?

Scan weekly, report monthly. AI answers vary run to run, so weekly sampling smooths volatility into a usable trend, while a monthly report gives clients signal instead of noise. A single one-time scan is a baseline, not a measurement program.

What is a good AI visibility score?

There is no universal benchmark — scores depend on the prompt set, methodology, and competitive category. What matters is a consistent methodology and the direction of the trend over three or more months, benchmarked against named competitors in the same prompt set.

How do you measure share of voice in AI answers?

Run a fixed set of buyer-intent prompts across the major AI engines on a schedule, record every business named in each answer, and compute the percentage of answers naming each business. Compared month over month, this shows which competitors are gaining or losing ground in AI answers.