How to Audit Your AI Footprint
Published 2026-04-08 · 11 min read · How-to guides
Overview
Run quarterly; store screenshots for trend analysis.
Quick definition
An AI footprint audit inventories how your brand appears across major assistants and AI overviews—scores accuracy, finds omission causes, and produces a prioritized fix list tied to pages and data sources.
Definition
Scope: branded and category prompts in key geos.
Why it matters
You need a baseline before claiming improvement.
Core framework
Prompt library
20–40 stable prompts.
Rubric
Correct / partial / wrong / omitted.
Step-by-step breakdown
Execute
Same day/time where possible; log model versions if visible.
Trace errors
Map wrong claims to a source URL or third-party profile.
Real-world examples
A SaaS team found 60% of errors traced to an old help subdomain.
Common mistakes
- One-off checks without documentation.
Related topics
Request an evaluation—PrimeAxiom runs footprint audits alongside automation assessments.