GEO audit method for SaaS
CiteKit GEO Audit Method
The repeatable audit method behind CiteKit's free AI search visibility tools, including scoring dimensions, evidence requirements, and limitations.
What the method evaluates
CiteKit evaluates whether a SaaS page is easy for humans, search engines, and answer engines to understand. The method does not claim to reverse engineer any AI system. It creates a practical review of visible evidence: can a crawler access the page, can the page explain the entity, can a short answer be extracted, can claims be verified, and can comparison questions be answered without guessing?
- Crawlability: public URL, indexable page, sitemap discovery, canonical clarity, and no accidental robots blocks.
- Entity clarity: product name, company, category, audience, features, integrations, pricing model, and support model are visible in copy.
- Answer extraction: important H2 sections begin with concise direct answers that still read naturally for people.
- Citation proof: screenshots, docs, changelogs, reviews, launch pages, third-party mentions, or public tutorials support claims.
- Comparison coverage: buyers can understand when the product is a fit, when alternatives are better, and what evidence supports the recommendation.
The five-point scoring model
Each audit dimension uses a simple 0 to 2 review score. Zero means the evidence is missing or blocked. One means the evidence exists but is incomplete, ambiguous, or hard to extract. Two means the evidence is visible, specific, and easy to verify. The total is not a ranking score. It is a prioritization tool for deciding which page updates should happen first.
| Dimension | Weak signal | Strong signal |
|---|---|---|
| Crawlability | Page requires login, is noindexed, or has conflicting canonicals | Public URL, indexable, canonicalized, and present in sitemap |
| Entity clarity | Category and audience are implied but never stated directly | Product, category, audience, pricing model, and integrations are explicit |
| Answer extraction | Sections open with vague marketing language | H2 sections begin with 40-80 word direct answers |
| Citation proof | Claims have no source or example | Claims are backed by docs, screenshots, changelogs, reviews, or third-party pages |
| Comparison coverage | Competitors are ignored or dismissed | Criteria explain who each option is best for and where it is weaker |
How an audit is run
A lightweight audit starts with one product page and one comparison or alternatives page. The reviewer checks technical access, extracts product facts, runs a fixed prompt set in answer engines, records mentions and citations, then maps missing evidence back to page sections. The same prompts are used again after updates so the team can compare results instead of reacting to one-off answers.
- Step 1: Check robots.txt, meta robots, canonical URL, sitemap entry, and visible page content.
- Step 2: Extract entity facts from the page and compare them with generated schema.
- Step 3: Run category, alternatives, comparison, integration, and pricing prompts manually.
- Step 4: Record whether the brand appears, where it appears, and which URLs are cited.
- Step 5: Update the page with missing direct answers, proof assets, schema, and comparison context.
What this method cannot prove
No public tool can guarantee a ChatGPT, Perplexity, Google AI Overview, or Bing Copilot citation. Answer engines can change retrieval systems, model versions, personalization, and partner indexes. CiteKit therefore focuses on evidence quality and repeatability rather than certainty. The method is useful when it helps a team publish clearer pages, identify missing proof, and avoid unsupported claims.
- It cannot prove future rankings or revenue.
- It cannot replace manual review of generated schema and copy.
- It cannot make a thin page valuable by formatting it differently.
- It cannot verify private data, unlaunched products, or claims that do not appear publicly.
Use this with a tool
Turn this page into a concrete review by starting with the visibility checker, schema generator, or crawler checker.