Turn an AI Presence Scan Into an Improvement Plan
Turn measured website, corroboration and machine-answer findings into focused improvements and repeatable checks.
Measuring Results
How to check AI recommendations with repeatable evidence instead of one-off answers.
How to Benchmark AI Recommendations Without Fooling Yourself
A practical design for repeatable AI recommendation tests that separates selection outcomes from provider failure and normal model variability.
Why AEO Measurement Needs Frozen Evidence
Repeatable AEO measurement depends on preserving the exact evidence, context, and availability state behind every conclusion.
A Practical 90-Day AEO Plan for Local Businesses
A staged three-month plan for improving business identity, public authority, AI-facing answers, and measurable recommendation readiness.
How to Measure Whether AI Can Recommend Your Business
A disciplined method for testing local recommendation questions without turning variable AI answers into misleading ranking claims.
What the RNX Protocol Measures
A practical overview of the RNX measurement layers without exposing scoring internals or implementation recipes.
Why AI Presence Needs an Operational Cadence
Why one-time AI readiness fixes decay unless businesses monitor domain, authority, and agent signals over time.