Executing a Generative Engine Optimization Audit for Brands

Step 1: Capture the baseline before touching anything

Build a prompt set: 15–25 discovery, comparison, and branded questions per brand. Run them across ChatGPT (browsing on and off), Perplexity, and Google’s AI features in clean sessions; save every full response. Score each: present or absent, cited or paraphrased, accurate or wrong, recommended or listed. This snapshot is the against-which for everything later — skipping it is the most common audit-invalidating mistake, because without it you can never prove movement.

Step 2: Read the results for focus, not despair

Before diving into fixes, find the shape of the problem. Cluster your failures: absent from discovery but fine on branded queries → a corroboration problem. Cited but described wrongly → an entity problem. Strong in AI Overviews but missing from ChatGPT → a Bing indexing problem. Visible nowhere → start at the technical gate. The cluster tells you where the next forty hours go; auditing everything equally wastes most of them.

Step 3: Map the entity and knowledge graph

Audit how machines resolve who the brand is: Organization/Person schema present and accurate? Name, offer, and facts identical across site, GBP, directories, and profiles? Knowledge-panel or Wikidata presence where warranted? Same-name confusion with another entity? Log every inconsistency — each one is a confidence leak — and define the canonical description that every surface should carry verbatim.

Step 4: Remediate schema systematically

Schema fixes scale best in two passes. Global blocks first: Organization (or LocalBusiness/Person) sitewide, correct and complete, plus llms.txt. Then per-template blocks: Article with named authors on posts, FAQPage on Q&A sections, Service/Product where applicable — each mirroring visible content exactly. Validate everything; schema that contradicts the page reads as manipulation and hurts rather than helps.

Step 5: Score content depth against citation potential

For each target question from step 1, grade the brand’s best existing page: Does any passage answer it directly and stand alone? Is the answer evidenced and dated? Does the page cover the follow-ups? Three grades result: citable now (minor structure fixes), fixable (right topic, wrong shape — restructure answer-first), and missing (no page owns the question — create one). This grading converts vague “content gaps” into a concrete build list.

Step 6: Audit the corroboration layer

From the step-1 responses, list every third-party domain the engines cited. Check the brand’s presence on each: reviews, listings, mentions, contributed expertise. The absences on domains the engines already trust are the highest-yield off-site work available — far ahead of generic link building. Note competitors’ footprints on the same domains; the deltas explain most citation losses.

Step 7: Ship the roadmap as a sprint plan

Sequence everything found by impact-per-effort: retrieval blockers first (they gate everything), entity fixes second (they multiply everything), then the citable-now structure fixes, then content builds, then corroboration campaigns — with owners and dates, not a hundred-item dump. Re-run the step-1 panel monthly and report movement against the baseline. Pitfalls that sink audits: no baseline (unprovable), one-engine testing (misses layer-specific failures), single-run conclusions (answers vary — trends only), schema divergence from visible content, and roadmaps sorted by ease instead of impact. This methodology — baseline, layered diagnosis, prioritized roadmap — is exactly what our Brand Audit packages, with the tooling to run it at depth; what the results typically look like is in what an AI search audit reveals.