Fide AI · Engagement brief
Evidence for your next AI decision.
Fide AI investigates AI model and agent behavior so teams can make a specific training, release, or deployment decision with evidence they can inspect.
A defined study can complement your internal evaluation team by examining a risk, comparing a change, or reviewing whether a measurement method supports the conclusions drawn from it.
Three starting points
Model and checkpoint comparison
Compare agreed model versions or mitigations under a consistent protocol. Investigate regressions, adversarial behavior, and usefulness tradeoffs, keeping development examples separate from held-out tests.
Typical outputs: Comparison protocol, traceable results, failure analysis, uncertainty, and a briefing on the implications for your decision.
Agent workflow assessment
Test one consequential workflow for faithful source use, permissions, delegation, and truthful action reporting. Examine realistic exceptions, adversarial inputs, and the points where a person should intervene.
Typical outputs: Workflow and authority map, evidence-backed findings, remediation priorities, and targeted retesting where commissioned.
Evaluation and grader validity review
Examine rubrics, reference judgments, grader reliability, and disagreements. Investigate false positives and negatives, coverage gaps, and sensitivity to changes in models or operating conditions.
Typical outputs: Measurement review, calibration analysis, explicit limitations, and recommendations for how to use the evaluation signal.
Scope and delivery
Agree the decision, intended users, model or system versions, relevant risks, available access, and technical or domain expertise. Define the protocol, outputs, test budget, schedule, and fee before work begins.
Depending on the study, outputs can include rerun materials, evidence-backed findings, uncertainty and limitations, remediation or retesting priorities, and a briefing for decision makers. A recurring evaluation cycle has explicit scope and capacity; changes to the system or risk areas may require a new scope.
Led by Alex Chao
Alex’s experience spans frontier-model post-training and evaluation at ByteDance Seed, generative-AI strategy and product incubation in Microsoft’s Office of the CTO, and statistical safety evaluation in Uber’s autonomous-driving division. Each engagement identifies additional domain expertise where the question requires it.
Fide’s published evaluations state their methods and limits. Results in one setting do not establish performance in another domain.
Responsibility and independence
Your team retains responsibility for development and deployment. Fide discloses relevant development involvement and conflicts. Testing changes we helped build is collaborative validation; independent conclusions about those changes require separate, non-conflicted review. Fees do not depend on favorable findings.
Data access, retention, intellectual property, research reuse, and publication are agreed separately. Conclusions are bounded by the intended use, methods, and available evidence. Evaluation is not a blanket safety guarantee or certification.
Read the independence policy →Start a conversation
Send the system or workflow, the decision, available access, and timing to [email protected].
Engagement information and published evidence: fideai.org/evaluations/.