Help build the evidence behind intelligence infrastructure.
KlearFlowAI Research Institute™ is assembling the founding Phase 1 cohort for the Intelligence Infrastructure Benchmark™. The purpose is to validate the research instrument and evidence anchors before any population-level benchmark claims are made.
Apply for the Founding Cohort25–50 organizations
The founding sample is intentionally directional. It is designed to validate questions, scoring anchors, evidence availability, assessor consistency, and dimension boundaries—not to manufacture a representative market percentile.
Leaders close to operating reality
CEOs/founders, CIO/CTO/CDAO leaders, COOs, AI/transformation leaders, systems and enterprise architects, and operational leaders responsible for multi-system AI deployments are strong fits.
Participation is not certification
Joining the research does not grant an ANO™ certification, a formal ANRS™ result, or a market ranking. Research findings and certification systems remain separate by design.
Thirty-two evidence items across eight infrastructure dimensions.
The research examines Knowledge Infrastructure, Intelligence Architecture, Decision Architecture, Execution Integration, Governance & Identity, Learning Infrastructure, Interoperability & Resilience, and Intelligence Flow & Velocity.
What an organization says and what the system shows are recorded separately.
Assertion only
A respondent describes the capability without corroborating evidence.
Partial evidence
One supporting artifact, metric, or independent corroboration is available.
Corroborated
Two or more independent forms of evidence support the finding.
Direct / system evidence
Logs, configuration, observed workflow behavior, or equivalent direct evidence supports the finding.
Structured conversation plus evidence review-not a generic survey.
Organization profile
Industry, size band, AI deployment pattern, functions using AI, and respondent role. Organization identity remains separate from the analytical dataset.
Evidence interview
A structured review of current operating practices, architecture, governance, learning, interoperability, and intelligence flow.
Supporting evidence
Where appropriate: policies, architecture records, workflow evidence, logs, inventories, operational metrics, or other minimally necessary corroboration.
Qualitative context
What scales, what breaks, where context is lost, what cannot yet be delegated, and what architectural change would most increase the ability to absorb AI capability.
De-identified analysis by default. Attribution only with explicit permission.
The research model separates participant identity, supporting evidence, and the analytical dataset. Organizations choose the permitted publication level-from no public reference through an explicitly approved named case. Sensitive research evidence does not flow into ordinary sales systems.
No fake percentile. No synthetic sample. No certification-by-participation.
Phase 1 results will be described according to the sample and evidence actually collected. Missing evidence is not automatically scored zero, contradictions are preserved for review, and single-respondent records are flagged.
Apply to contribute operating evidence to the founding benchmark.
Applications are screened for fit, respondent proximity to operating reality, and the ability to participate in structured evidence review. Submission does not guarantee inclusion.