Sample report

Audit Lite sample for a form-backend product.

This sample uses a developer-tool category and public-style findings. It is not a customer case study.

Executive finding

Understood when named, but weaker in open discovery.

The product is described correctly when directly compared, but broad buyer prompts can omit it while naming better-known alternatives. This points to a public evidence and category retrieval gap, not necessarily a product-quality issue.

Sample score 58 Usable but uneven
Visibility16 / 30
Description accuracy18 / 25
Evidence strength9 / 20
Competitive position6 / 15
Fixability9 / 10

Test setup

FieldSample value
CategoryStatic-site form backend / form-to-email API
TargetSample developer tool
CompetitorsFormspree, Formcarry, Basin, Getform, Netlify Forms
SurfacesGemini and Perplexity where available
Blocked surfacesMarked as unavailable rather than substituted

Key evidence

Prompt stateObserved signalInterpretation
Open discovery Target absent while competitors appear in adjacent suggestions. Likely visibility or recall gap in broad buyer selection.
Specific requirements Target appears when the buyer asks for spam protection, setup simplicity, and free tier. Positioning exists, but may not be retrieved by default.
Named comparison Assistant understands the product, but one security phrase is too specific. Baseline product facts should be tightened.

Recommended roadmap

01 Create a concise AI-readable product facts page.

Reduce ambiguity in category, ideal buyer, free tier, setup time, and security boundaries.

02 Tighten FAQ wording around spam protection and access-key safety.

Prevent assistants from inventing overly specific security wording.

03 Build honest comparison pages against direct alternatives.

Give assistants clearer first-party evidence for buyer comparison prompts.

04 Retest the same prompt set after changes are public.

Measure directional change without claiming guaranteed recommendation lift.

Boundary statement

This report does not prove that changes will increase AI recommendations. It shows current behavior, identifies likely evidence gaps, and gives a practical roadmap for making the product easier to understand and compare.