Manual where it matters, automated where it scales. Every audit follows the same six steps so findings are comparable across categories and over time.
AI visibility — whether AI answer engines mention, recommend, omit, or misrepresent your brand for buyer-intent prompts, and the public evidence that causes them to do so.
Classic search optimization asks “where do we rank for this keyword?” AI search answers a different question: when a buyer asks an engine to recommend tools for a job, what does it say — and why? Our methodology is built to answer that with evidence, not vibes. We do not optimize for a hidden ranking we can’t see. We measure what engines actually return, trace it to the sources and pages behind it, and hand you a roadmap to improve that evidence.
The method is deliberately bounded and repeatable. A locked prompt set, named competitors, named engines, human labelling, and a fixed retest cadence mean two runs of the same audit can be compared honestly. That’s what makes a delta meaningful.
Same sequence, same artifacts, every time. Below the overview, each step is broken down in detail.
10–15 prompts for a Snapshot, or 25–50 prompts for Monitoring, mapped to persona, funnel stage, and category — not vanity queries.
We interview your team and study the category to write prompts the way buyers ask them — mapped to persona and a fixed awareness/consideration/decision mix. Vanity queries are excluded.
3–5 named competitors across 3–5 AI/search surfaces (ChatGPT, Perplexity, Gemini, Claude, Google AI Mode).
Competitors are named explicitly (no “the market” hand-waving), and engines are chosen by where your category's buyers actually ask. Both are recorded in the report.
Timestamped captures, screenshots, source URLs, and engine metadata preserved for every prompt.
Every prompt run is captured with a screenshot, source URLs, engine metadata, and a timestamp. Because AI answers are non-deterministic, we rely on patterns across the full set, not a single response.
Recommendation, mention, omission, misrepresentation — labelled by a human reviewer, not a heuristic.
A human reviewer labels each capture: recommended, mentioned, omitted, or misrepresented. Edge cases are documented, not silently scored.
Which third-party and owned pages drive citations, and which gaps your competitors are filling.
We map which third-party and owned pages drive citations, and which sources competitors own that you're absent from — the gaps that explain most omissions.
A sequenced fix backlog with impact/effort, plus a retest plan to measure visibility gained.
Findings become a backlog sequenced by impact and effort, each fix tied to the prompts it should move, plus a 30/90-day retest plan to measure visibility gained.
Every captured answer gets exactly one label. Metrics are always reported as a count over the prompt-set size (e.g. 12 / 40), never as a bare percentage.
The engine actively suggests your brand as a choice for the prompt (“you should look at …”, or it appears in a ranked shortlist). The strongest, most defensible signal.
Your brand is named without a recommendation — listed in passing, used as an example, or referenced in a citation. Visible, but not yet a buying signal.
Your brand is absent from a relevant answer where direct competitors appear. The default failure mode, and the largest single category in most first audits.
Your brand is present but the claim is inaccurate or stale (wrong price, missing integration, wrong audience). The highest-cost error because the buyer trusts a confident, wrong answer.
Because a single prompt can recommend several vendors, competitor shares need not sum to the prompt count. We state the denominator on every chart so the math is always checkable.
[1–3] Aggregated industry research, 2025–2026. Full sources listed below.
The stat band above aggregates industry research; figures are directional and cited so you can check them. The fundamentals we build on come from Google’s own guidance for AI search experiences.
We do not present aggregated figures as precise measurements of your category. Your audit’s numbers come only from your own captured runs — see a full sample report.
It's AI visibility — measuring whether AI answer engines recommend, mention, omit, or misrepresent you for buyer-intent prompts. SEO fundamentals (crawlable pages, useful content, accurate structured data) still apply; we layer AI-specific evidence on top.
The prompt set is locked before testing and mapped to persona and funnel stage, with a fixed awareness/consideration/decision mix. We reuse the identical set on every retest so results are comparable over time, not curated.
A human reviewer, not a heuristic. Each captured answer is read in context and labelled. Edge cases (a brand named only to dismiss it, for example) are documented in the report rather than silently scored.
Every finding records the engine, the prompt ID, and a capture date, with a screenshot. AI answers are non-deterministic, so we note that explicitly and rely on patterns across the full set rather than any single response.
No, and you should be skeptical of anyone who does. We measure visibility, identify gaps, and help you improve the public evidence engines can understand. That's the only honest service.
The sample report walks the full output of these six steps on a fictional category, end to end.