The AI Visibility Assessment — how thoroughly we measure a clinic
Plain-English briefing. The assessment is the front door of the product and the evidence base for everything that follows. Written 24 July 2026. Last verified: 19 August 2026 (Agent 11).
What it is
The AI Visibility Assessment answers one question with evidence: how visible is this clinic to AI search today, and exactly why? It is not a quick scan or a single-tool score. It is a multi-source, multi-stage audit that produces a benchmarked, itemised picture of the clinic's standing — and every subsequent recommendation the platform makes traces back to something the assessment measured.
The reason for the thoroughness is simple: the content plan only works if it targets what actually moves the needle for this specific clinic. Random content creation is the industry default; evidence-directed content creation is the product.
What gets measured
1. The full website, page by page. We crawl the clinic's entire site and build a complete page inventory: every URL, its title, headings, and content. Each page is classified by type (condition page, treatment page, service page, blog post, team page, structural page) and individually scored against our quality checklist. This isn't a homepage-only skim — a 147-page clinic site gets 147 individual page evaluations.
2. Quality scoring against a 71-criterion, research-grounded checklist. Every page is scored 0–100 against a unified checklist derived from published research into how AI search engines select and cite sources. The criteria span: identifiable authorship and credentials, structured data (schema) presence and correctness, evidence citations, content depth and answer-first structure, freshness signals, internal linking, machine readability, and clinical trust markers. The same checklist scores the clinic's existing pages, scores every page we later generate, and gates publication — one consistent quality standard through the whole platform.
3. Live AI visibility probing. We don't guess whether AI cites the clinic — we ask. The assessment runs a fixed benchmark set of realistic patient queries against AI search engines and records whether and where the clinic is cited. This produces a real citation rate (e.g. "cited in 15% of relevant queries"), tracked against the same fixed query set over time so movement is genuinely comparable. The benchmark set is deliberately held stable, with occasional curated additions as the clinic's content expands.
4. Reviews and reputation. Google Business Profile review data is collected and analysed: volume, average rating, velocity. Reviews are one of the strongest local trust signals AI search uses, and they also feed the structured data we later attach to the clinic's content (a real aggregate rating — never fabricated; if a clinic has no reviews, no rating is claimed).
5. Traditional search positioning. Search-engine results positioning for relevant local healthcare queries, via an independent SERP data provider — because traditional and AI search visibility interact, and because ranking data feeds the prioritisation of which pages to refresh.
6. Search demand data. Search-volume enrichment from an independent keyword data provider, so plan prioritisation can weigh real patient demand rather than guessing which topics matter.
7. Structured data audit. Whether the site carries the machine-readable schema that lets AI engines confidently parse who the clinic is, who its practitioners are, what conditions it treats — and whether that schema is correct, complete, and consistent with the visible content.
8. Local entity and competitive context. Discovery of relevant local entities and how the clinic sits within its local healthcare landscape.
9. The team. Practitioner detection and profile analysis: who works at this clinic, what credentials are visible, how complete and machine-readable each practitioner's professional identity is. Because AI trust is anchored in identifiable people, practitioner profile depth is measured as a first-class part of clinic visibility — not an afterthought.
10. Deep research backstop. Where automated collection can't reach (unusual site structures, sparse data), a deep-research agent supplements the automated pipeline, so an unusual site still gets a complete assessment rather than a degraded one.
Belt and braces: multiple independent sources
No single tool's opinion decides anything. The assessment cross-references: our own full crawl, live AI-engine probing, Google Business Profile data, an independent SERP provider, an independent search-volume provider, the DOI registry (for citation verification downstream), and deep-research supplementation. Where sources disagree or data is missing, that is surfaced honestly — the platform flags "needs verification" rather than silently guessing. The internal design rule is: no fabrication, anywhere, ever. A clinic with no reviews shows no rating. A page that can't be verified isn't marked as fine.
What the customer sees
The output is a scored report the customer can actually read: an overall site health score, an AI visibility score with the real citation rate behind it, what's working well, priority improvements in plain language, per-category breakdowns (technical foundations, local trust signals, professional credibility, content and discovery), and the complete page inventory with individual page scores. Nothing is a black box — an admin can drill from the headline number down to the specific criterion a specific page failed.
Benchmarking — the score history is sacred
Every assessment writes a dated snapshot that is preserved permanently. When the clinic re-measures (a lighter periodic re-run that reuses the stored discovery, re-crawls the pages, refreshes the score-driving inputs, and writes a new dated snapshot), the new result sits alongside the old one — never replacing it. That preserved history is the customer's improvement story: "You were 52% in March. You're 67% now. Here's what changed." The platform treats destroying benchmark history as the cardinal sin, because that history is the customer's proof the work is paying off.
How this connects to everything else
- The content plan (see 02-content-plan.md) is composed from assessment outputs: page inventory, page scores, cluster coverage, practitioner data, review signals, demand data.
- The quality gate on generated content uses the same checklist the assessment used to score the clinic's existing pages — one standard, end to end.
- The re-measure loop (see 05-publish-and-measure.md) closes the circle: publish content, re-measure, watch the same instruments register the improvement.
Common questions this briefing answers
- "Is this just an automated site scan?" No — it's a full crawl plus live AI probing plus review/SERP/demand data from independent providers plus practitioner analysis, benchmarked and preserved over time.
- "How do you know what AI search rewards?" The scoring checklist is grounded in published research on AI source selection, and the live probing measures actual citation behaviour rather than theorising.
- "Will my score be comparable next quarter?" Yes — the AI-probe query set is held fixed as your benchmark, snapshots are additive and dated, and re-measures score with the current algorithm while preserving your full history.
- "What if my site is unusual — not WordPress, oddly structured, tiny?" The pipeline handles non-standard sites, and the deep-research backstop covers what automation can't reach. Flat or unusual structures are assessed honestly, with any limits made visible rather than papered over.
- "Did profile scores change recently?" Yes — profile scoring was recalibrated in August 2026 (with an in-app note): points moved toward what makes a clinician genuinely distinctive, such as condition and technique depth, patient groups and life beyond the clinic. Nobody lost author or reviewer status from the rescore. The current content-unlock rule: one active clinician at 60%+ (the author) plus a different active clinician at 50%+ (the reviewer) unlocks the content plan — unlock is sticky, and once unlocked any clinician can author at any score.