AI recommendation monitoring and GEO optimization

Research before conclusions.

Reliable AI recommendation monitoring requires definitions, observations and calibration that remain useful as models, products and retrieval conditions change.

Current research program

Protocol 01

AI Readiness Benchmark 2026

Sampling frame, metric strata, calibration plan and participation criteria for an interpretable industry benchmark.

Method 01

Measurement Method V1

Evidence records, repeated sampling, human review, AI evaluation and regression tests.

Field note 01

GEO growth system

A durable operating connection between crawlability, evidence, representation and agent action.

Technical note 01

Machine-readable content for AI agents

How llms.txt, Markdown counterparts and page-level links create a clearer route to approved public facts.

Questions the research must answer

  • Which capabilities predict accurate citation across multiple AI systems?
  • How much observed movement is intervention effect versus normal model variance?
  • Which dimensions transfer across industries and which require sector norms?
  • When do AI judges agree with trained human reviewers — and when do they fail?
  • How should agent task completion change the readiness model?
  • What evidence would falsify the claim that AI Readiness is an independent category?

Publication standard

Every research release will name its sample, exclusions, method version, observation window, missing data and conflicts of interest. Raw or minimally transformed evidence will be published when consent, confidentiality and platform terms allow it.