Evaluate
Test systems and claims
Frame the question, build the dataset, and preserve the evidence behind every result.
Applied research
Make consequential decisions explainable, reviewable, and governed.
Turnkeeper connects evaluation, evidence contracts, and human review so teams can test AI systems, preserve provenance, and govern how recommendations become consequential actions.
From question to outcomeEvidence · review · governance
QuestionEvidenceEvaluationProvenanceHuman reviewOutcome
Consequential AI decisions are not one model output. They depend on three connected layers: evaluation, evidence, and human governance.
Turnkeeper keeps the path between them transparent, auditable, and accountable.
Connect
Trace evidence and provenance
Carry source, dependency, uncertainty, and counterevidence into review.
Govern
Keep people in control
Separate recommendations, human judgment, authorization, and customer-owned outcomes.
Research ledger
Current work
Browse the questions we are testing, the evidence behind each program, and what remains unproven. Every entry names its present evidence status.
0104
Turnkeeper Evals
Implemented · hosted workspace
Research questionHow can teams trust and reproduce AI evaluations?View research
Evidence trace · 01
Evidence bound · gates resolved · human release review
Current evidence statusImplemented · hosted workspace
Showing Turnkeeper Evals, entry 1 of 4.
Operating boundaries
Boundaries before breakthroughs.
Evidence before claims
We make claims only as strong as the evidence supporting them.
Privacy minimized by design
Use only the information necessary to test the question.
People decide
Research may inform judgment. It does not make consequential decisions.
Research informs. People decide.
Work with the lab
Bring us a consequential AI workflow worth examining.
Map a real workflow, test a question with synthetic or privacy-minimized evidence, or challenge an assumption in the current work.