Sample GEO audit for a B2B manufacturing page
This is the kind of report a user sees after running GEO Optimizer Pro. The example is based on a fictional EV battery assembly equipment page, so the structure is useful without exposing customer data.
Snapshot
The report separates content problems from technical access problems so teams can assign fixes cleanly.
Content citability: 62
The page describes the product, but it does not define who the product is for, what measurable outcomes it improves, or which specs an AI answer can safely cite.
Technical GEO: 76
Robots and sitemap signals are acceptable. Missing llms.txt, thin Organization schema, and weak page-level Product schema reduce machine-readable confidence.
Query coverage: partial
The page could answer "EV battery module assembly line supplier" but not "how to choose a prismatic cell PACK line" or "battery module welding process."
Recommended fixes
Each fix is written so a content editor or developer can act without translating a vague score into work.
Add a sourceable product definition
Start the page with a clear one-paragraph definition: what the line does, target battery format, automation scope, and buyer type.
A prismatic cell PACK assembly line automates cell sorting, module stacking, busbar welding, insulation testing, and final pack inspection for EV and ESS manufacturers.
Give AI engines citable evidence
Add inspection metrics, process stages, compatible cell formats, factory layout constraints, and after-sales coverage. Avoid claims that cannot be verified from the page.
Add llms.txt and richer schema
Create a short llms.txt file pointing AI agents to product pages, docs, case studies, and contact paths. Expand Organization and Product schema to include brand, category, model, and support area.
Build an answer section for buyer questions
Add concise answers for pricing factors, machine footprint, cycle-time dependencies, welding method, cell format compatibility, and integration timeline.