AI visibility audit for medspas

1 · What the audit measures

For each medspa, the audit produces a per-surface visibility score: the share of patient-intent queries on that surface where the medspa is named in the answer, weighted by rank position when multiple competitors are named. The audit measures three quantities per query × surface pair:

• Mention. Was the medspa named in the AI’s answer?
• Citation. Was the medspa’s URL or property cited as a source?
• Rank position. Ordinal position among named competitors (1st, 2nd, 3rd…).

Per-surface scores roll up into a single combined index. The full normalization rules and dedup logic are documented in our methodology.

2 · The query basket

Thirty-one patient query types per medspa, expanded into one hundred forty query-runner combinations to capture LLM variance. The 31 types cluster into six intents:

• Treatment-led: “best botox in Sunnyvale”, “best lip filler in Sunnyvale”.
• Location-led: “Sunnyvale medical spa”, “Sunnyvale aesthetic clinic”.
• Comparative: “[your medspa] vs [rival]”, “[brand] vs [brand]”.
• Trust-led: “safest place for botox”, “most reviewed medspa”.
• Cost-led: “how much does botox cost in Sunnyvale”, “lip filler pricing in Sunnyvale”.
• Research-led: “how to choose a medspa in Sunnyvale”, “is medical weight loss worth it”.

3 · What you receive

A printed PDF, walked to the front desk inside 24 hours of intake, containing:

• Per-surface visibility score (0–100) for ChatGPT, Claude, Gemini, Google AI Overviews, and Perplexity, plus a combined index.
• Side-by-side competitor table: the top three named competitors per intent cluster.
• Ranked fix queue: the five changes that move the score, ordered by projected lift, not by activity volume.
• A 30-day visibility projection if the fixes are implemented as written.

4 · How the audit is run (5 steps)

1. Intake. A 15-minute call or written intake captures the medspa’s name, address, primary treatments offered, and patient catchment zip codes. No technical setup required from the client.
2. Measure. We run 31 patient query types, expanded into 140 query-runner combinations, against all five AI surfaces: ChatGPT, Claude, Gemini, Google AI Overviews, and Perplexity. Each combination is captured with the surface’s actual response and any citations.
3. Score. For each query × surface combination, we record mention, citation, and rank position. Variant brand spellings collapse to a single entity. We normalize to a 0–100 per-surface visibility score and a combined index across all five surfaces.
4. Rank fixes. We map the visibility gaps to specific, observable signals each AI surface weights. The recommendations are ordered by projected lift, not by activity volume. The output is the five changes that move the score, not a 40-item checklist.
5. Deliver. A printed PDF is walked to the medspa’s front desk within 24 hours of intake. It contains the per-surface scores, side-by-side competitor table, ranked fix queue, and a 30-day visibility projection if the fixes are implemented.

5 · Pricing

Founding rate is $1,497 setup and $497/month, locked for the first 5 South Bay medspas. Standard pricing after the founding seats are claimed is $3,500 setup and $997/month. Whichever rate you sign at is locked for the life of the engagement.

We commit to a 30% visibility increase across all five AI surfaces, as defined in our terms, within 90 days; if we miss, we refund your most recent month’s retainer. The setup fee is non-refundable, since the deployed infrastructure stays yours.

6 · What we don’t claim

• LLM outputs are stochastic. We run multiple combinations per query type to smooth this; two audits a week apart can still disagree at the margin on lower-scoring brands.
• The audit is a point-in-time snapshot. AI surfaces update continuously; we re-score monthly for active clients.
• We do not guarantee position-1 placement on any individual query. We optimize for the signals AI surfaces weight, not for any single query result.
• Perplexity is the fifth audited surface, added in June 2026 once it became a primary local-discovery answer engine. Its citation behavior is Reddit and UGC heavy for local queries, which we account for in scoring.

For a longer-form view of how the five AI surfaces differ from one another (and why we treat Google AI Overviews separately from Gemini) see our explainer on AEO for medspas.