GEO Audit
GEO audit: measure brand visibility in AI answers
A GEO (generative engine optimization) audit tests whether generative engines recommend your brand on real buyer questions — not whether a model can define you by name. This page is the reference for what a GEO audit checks, how to run one, and a free BuyerPrompt baseline on ~20–30 prompts.
What a BuyerPrompt audit surfaces
- Buyer prompt
- “best GEO audit tools for B2B SaaS brands”
- Model answer (excerpt)
- Most answers name category leaders and review sites first. A weaker GEO presence often means your brand never appears in the shortlist, while competitors and directories get the recommendation and the citation.
- Brands named instead
- Peec AI · Profound · Scrunch
- Cited source
- g2.com · producthunt.com · competitor comparison pages
- BuyerPrompt recommendation
- Publish a crawlable comparison page that answers the exact buyer prompt in plain language, then re-test the same prompt set to see whether recommendation and citation rates move.
Live product self-audit (14 Aug 2026): GEO audit tools compared. Method: methodology.
What a GEO audit checks
- Unbranded category prompts — “best X for Y”, not only “what is [your brand]”
- Recommendation vs mention — named in a shortlist vs a passing reference
- Citations — which domains the model treats as evidence (docs, G2, Reddit, comparison pages)
- Competitor gaps — who wins the same buyer prompts
- Re-test readiness — a frozen prompt set you can run again after you publish
Real GEO audit example (OpenAI, 72 answers)
Public multi-brand GEO-style panel on AI code review tools (21 Jul 2026, run BP-CODE-20260721-04): 72 answers, 3 runs per prompt, 75% of answers cited at least one source, 46 unique domains, 270 citation events. Leader on stable unbranded coverage: GitHub Copilot (61%).
| Brand | Stable unbranded coverage | ≥2 of 3 runs |
|---|---|---|
| GitHub Copilot | 61% | 11/18 |
| CodeRabbit | 56% | 10/18 |
| Qodo | 56% | 10/18 |
| Greptile | 39% | 7/18 |
| Graphite | 28% | 5/18 |
| Ellipsis | 0% | 0/18 |
Source: BP-CODE-20260721-04 — 72/72 OpenAI answers, 3× per prompt, published 21 Jul 2026. Numbers are a snapshot; assistants change.
Citation gaps the GEO audit surfaces
Engines do not invent authority from a homepage slogan. In the same dataset, docs and product domains dominate citations:
| Cited domain | Times cited |
|---|---|
| docs.coderabbit.ai | 33 |
| greptile.com | 31 |
| github.com | 26 |
| docs.github.com | 23 |
| docs.qodo.ai | 19 |
| qodo.ai | 19 |
| coderabbit.ai | 16 |
| graphite.com | 13 |
Source: BP-CODE-20260721-04 — 72/72 OpenAI answers, 3× per prompt, published 21 Jul 2026. Numbers are a snapshot; assistants change.
GEO audit snapshot: BuyerPrompt vs monitors (14 Aug 2026)
We ran our own product through a 25-prompt OpenAI GEO audit. Rates below are that snapshot only. BuyerPrompt: 100% mention on branded prompts, 0% on unbranded category/problem prompts — the gap a GEO audit is meant to find.
| Tool | Type | Mention | Recommend |
|---|---|---|---|
| Peec AI | GEO monitor | 40% | 32% |
| Profound | Enterprise GEO monitor | 32% | 24% |
| BuyerPrompt | Snapshot GEO audit | 32% | 16% |
| Scrunch | GEO monitor + agent delivery | 28% | 16% |
Full comparison and prices: GEO audit tools.
How to conduct a GEO audit
- Collect brand, category, alternatives, comparison, and problem prompts (~20–30).
- Run them through ChatGPT with web search (then other engines if needed).
- Score mention, recommendation, and citation — not a vanity composite alone.
- List domains the model already trusts (docs, G2, comparison pages).
- Publish extractable pages for the prompts you lose; re-test the same set.
Technical GEO audit checklist
- Crawlable HTML for “best X / X vs Y” answers (not only a JS-only app shell)
- Clear product facts: pricing, ICP, integrations, limitations
- Named comparison and alternatives pages matching lost prompts
- Docs or FAQ pages models can quote without inventing
- Consistent brand entity across site, schema, and directories
- A frozen prompt set + dated re-test plan (not a one-off ChatGPT chat)
GEO audit vs traditional SEO audit
An SEO audit asks whether you rank in a list of blue links. A GEO audit asks whether you appear inside the generated answer — often with a citation and a recommendation. Signals overlap (clarity, crawlability, authority), but the unit of success is different: extractable evidence on buyer prompts, not only SERP position.
GEO and AEO
Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) describe the same job. BuyerPrompt measures mention / recommendation / citation regardless of the label. Free baseline: OpenAI. Paid unlock: Perplexity, Gemini, Claude, and Grok on the identical prompt set.
Run your free audit
Free starts on OpenAI with native web search; upgrade unlocks more engines. See a sample report first if you want.
FAQ
What is a GEO audit?
A GEO audit tests buyer-intent prompts against generative engines and reports whether your brand is mentioned, recommended, and cited — plus who wins the same questions and which sources the model relies on.
How do I conduct a GEO audit?
Freeze ~20–30 buyer prompts (brand, category, alternatives, problem), run them through ChatGPT with web search, score mention/recommendation/citation, list cited domains, publish pages for the gaps, then re-test the same set. BuyerPrompt automates that loop.
What is a technical GEO audit?
The technical layer: crawlable comparison pages, extractable facts, docs/FAQ, entity consistency, and a re-testable prompt set. It complements content strategy; it is not a substitute for measuring actual assistant answers.
Is GEO the same as AEO?
Functionally yes — both optimize for AI-generated answers rather than a ranked list of links. BuyerPrompt measures the same signals either way.
Which engines count as generative engines here?
Free audit: ChatGPT (OpenAI API + web search). Paid upgrade: Perplexity, Gemini, Claude, and Grok on the identical prompt set.
Does a higher GEO score guarantee more pipeline?
No. The audit measures assistant visibility on relevant buyer prompts. Pair it with conversion and revenue data.