Model Evaluation & Validation
Build the evaluation system that tells you whether an engineering change works.
Who commissions this
A defined technical problem.
Model and product teams establishing acceptance tests, investigating failures, or comparing model and system versions.
Engineering scope
What the work covers.
- Translate requirements into representative tasks, scoring rubrics, and measurable failure conditions.
- Separate development and held-out data; inspect contamination and label ambiguity.
- Calibrate automated judges against human review and inspect disagreement.
- Integrate regression tests, versioned traces, subgroup analysis, and release thresholds.
Representative deliverables
Work your team can inspect and use.
- Versioned evaluation dataset and scoring protocol
- Evaluation harness with traceable results
- Failure taxonomy and prioritized remediation backlog
- Release gates and regression reporting
A measurable outcome, scoped together.
An engineering feedback loop for improving a system. For independent investment or procurement review, commission AI assurance separately.
Typical engagements begin at $50,000. Before a proposal, we agree the business objective, data access, system boundaries, deliverables, acceptance criteria, and operating owner. Timelines depend on scope and access.
Related technical evidence
Production AI architecture and inference economics · How to verify an AI performance claim
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