AI-citation reporting · Methodology
AI-citation reporting measures whether AI assistants cite or recommend your brand when buyers ask them questions, reported per engine over time. Point Visible runs it as a published method you can check. Most “AI visibility” reporting fails one test: can the client check it? This page documents the full protocol (the prompts, the engines, the logging, and the places where the method has limits) so every number in your report is checkable.
Protocol v1.0 · updated 2026-07-18 · fixed prompt set · logged model versions · movement, not attribution
01 · Why we publish this
“AI visibility” reporting has earned its reputation for vagueness: screenshots without prompts, scores without model versions, dashboards without timestamps. If you can’t see how a number was produced, you can’t tell measurement apart from marketing.
So we publish the protocol. Every figure in a Point Visible report traces back to a logged run: the exact prompt, the engine, the model version, the timestamp. You can audit any data point in your report against that log, and you can audit our method against this page.
02 · How measurement works
For your category we build a set of roughly 20–25 buyer-intent questions, the questions a real buyer would put to an AI assistant before a purchase decision. The set is frozen at your baseline run and stays identical every quarter. A question list that changes between runs can show whatever a vendor wants it to show; a frozen one can’t.
Every prompt runs against ChatGPT, Claude and Google AI Overviews. The three cite differently, so results are reported per engine. Collapsing them into one “visibility score” hides more than it shows.
AI engines ship updates constantly. Without version logs you can’t tell whether movement came from your coverage or from a model change. Every run in your report records the model version and the timestamp, so you can see exactly which runs straddle an update.
The same prompt on the same engine can return different answers minutes apart. We run each prompt repeatedly per engine and score across the full set of runs. One lucky answer does not count as presence.
Measurement re-runs each quarter, and the results are plotted against the dates your links and mentions went live. Movement and work always appear side by side, on the same timeline.
| Parameter | Specification |
|---|---|
| Prompt set | ~20–25 buyer-intent questions per client category · frozen at baseline |
| Engines | ChatGPT · Claude · Google AI Overviews |
| Logged per run | Model version · timestamp |
| Sampling | Each prompt run more than once per engine · counts as present only when the brand is cited or recommended in the answer, scored across the runs (not a single sample) |
| Cadence | Quarterly · aligned to placement dates |
| First run | Free baseline · quality-checked · usually ready in minutes |
03 · What we report
Citation presence, who is cited instead, movement versus your baseline, and the placement dates, each reported per engine.
For each prompt on each engine: is your brand cited or recommended in the answer. Scored across the repeated runs, not a single sample.
The competitors and publications occupying the answers you’re absent from. This is where the next quarter’s placement targets come from.
Change per engine since your baseline run: up, down, or flat. Flat is reported as flat.
Every movement chart is annotated with the dates placements went live, so you can judge the correlation yourself.
The pledge
We report movement, not attribution. AI answers shift for reasons beyond any single link: model updates, competitors’ coverage, community discussion. We show you the movement and the work, side by side, and let the correlation speak. We will show you flat results when they are flat.
04 · Known limitations
No AI-answer measurement is free of these. We list them so you can read your report with the same caveats we apply when we write it.
Limitation 01
The same prompt, same engine, same day can return different answers. Repeated runs narrow the variance; they don’t eliminate it. Treat single-prompt differences as noise and consistent movement across the set as signal.
Limitation 02
A major model update can shift answers across the board overnight. When that happens, quarter-over-quarter comparison weakens, and the report says so. The logged model versions show exactly where the break sits.
Limitation 03
What an engine shows you personally can differ from what it shows our measurement runs: answers vary with location, history and session. We measure under consistent conditions, but no setup reproduces every individual user’s context.
05 · How the free baseline relates
The free AI-citation baseline is not a lite version. Same prompt-set construction, same three engines, same version and timestamp logging. Quality-checked before it reaches you, usually within minutes. All it takes is two fields: your domain and a work email.
On the Citable Authority retainer ($3,950/mo), the baseline is re-run quarterly and every report measures movement against it. The pricing page lists what that retainer includes. Either way the baseline is yours to keep: it’s a real measurement, client or not.
The same protocol documented above, run once for your domain, free, and quality-checked before it goes out.
No call required · usually ready in minutes · yours either way