How to Run a Full AI Visibility Audit (Step by Step)

A full AI visibility audit establishes, with numbers, where your firm stands in AI answers: which queries you appear for, who appears instead, and which sources drive those answers. It is the baseline every measurement program — and our 60-day guarantee — is judged against. Here is the complete methodology.

Step 1 — Build the query universe (100–300 queries)

Start from buyer language, not keyword tools. Sources, in order of value:

1. Intake calls. The exact phrasings prospects use when they describe their problem.
2. Practice areas × locations. "Best [specialty] in [city]" for every combination you serve.
3. Problem-first questions. "What kind of lawyer do I need for X" — prospects who do not yet know the category.
4. Comparison and validation queries. "[Your firm] reviews," "[Your firm] vs [competitor]," "is [firm] good."

Aim for 100 queries for a single-market firm, 300+ for multi-market. Then freeze the set — comparability across months depends on it.

Step 2 — Sample correctly

For each query, on each engine (ChatGPT, Perplexity, Google AI Overviews at minimum):

• Run in a clean session — logged out, no memory, no history.
• Sample three times minimum per cycle. AI answers vary between runs; appearance rate is the datum, not a single appearance.
• Record the full answer, not just your status — competitor data is half the audit's value.
• Capture the citation panel: every source the engine used.

A 100-query audit across three engines at three samples each is 900 runs. This is why real programs automate it — but the methodology is identical done by hand at smaller scale, as in our 15-minute version.

Step 3 — Score every answer

Use a consistent rubric so the number means the same thing every month:

Appearance	Score
The recommendation, or first named	1.0
Named among options	0.5
Mentioned with caveats	0.25
Absent	0

Your share of answers is the average across all query-engine-sample combinations. Report it overall, per engine, and per practice area — the breakdowns reveal where the problem actually lives. Full scoring details in the share of answers methodology.

Step 4 — Map the citation graph

For every answer where a competitor appears and you do not, log the cited sources. After a few hundred runs, a pattern emerges: a shortlist of 10–30 domains — directories, review platforms, local press, Reddit threads — that drive the recommendations in your market.

That shortlist is the audit's most actionable output. It is not a generic "get listed everywhere" recommendation; it is the specific, evidence-based set of placements that move answers in your market.

Step 5 — Diagnose the failure mode

Audits surface one of four conditions, each with a different fix:

• Entity failure. Engines cannot resolve who you are — inconsistent names and addresses across the web, no structured data. Fix first; nothing else works without it.
• Citation absence. Engines know you but no trusted source vouches for you. The citation-graph shortlist from Step 4 is the work plan.
• Extraction failure. Your site is retrieved but never quoted — answers are buried in narrative prose. Fix with content engineered for extraction.
• Volatile presence. You appear inconsistently. Usually thin source coverage: one or two citations carrying you. Broaden the base.

Step 6 — Set the cadence

Re-run the identical audit monthly. Report share of answers against baseline, per-engine movement, queries won and lost, and the citation changes behind each shift. Expect Perplexity to move in weeks, ChatGPT in months — and expect volatility, since engine source-mixes have shifted by double digits within single quarters.

Every Result.st engagement opens with this audit across 200+ queries, and the monthly re-measurement is the number our guarantee is judged on. Contact us to see your baseline.