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.