Services · AI Model Evaluation
AI Model Evaluation
Independent assessment of model behavior across bias and fairness, robustness under distribution shift, output consistency, and performance degradation over time. We build evaluation suites that run continuously rather than as one-time assessments, so drift surfaces in production monitoring rather than in an audit.
What AI model evaluation covers
Bias and fairness assessment
Evaluation of model outputs across demographic groups and protected characteristics to identify systematic disparities in accuracy, error rates, or outputs. Includes statistical parity, equalized odds, and calibration analysis.
Robustness under distribution shift
Testing model behavior when inputs deviate from training distribution — seasonal shifts, user population changes, upstream data changes. Identifies brittleness before it manifests as production failures.
Output consistency
Assessment of output stability across semantically equivalent inputs, testing whether small variations in phrasing or format produce inconsistent or contradictory outputs.
Performance degradation over time
Continuous monitoring of model metrics across production traffic to detect drift — the gradual divergence between deployment-era inputs and training-era inputs that causes silent performance decline.
Evaluation suite design
For teams without existing evaluation infrastructure, we design and implement test case libraries, metric definitions, and monitoring pipelines that make evaluation a continuous practice rather than a periodic exercise.
How we approach it
Evaluation starts with the specific failure modes that matter for your deployment context. A credit decision model faces different bias risks than a clinical triage model, and the evaluation framework reflects that. We define the outcome dimensions, build or adapt the test dataset, run the assessment, and deliver findings with statistical grounding rather than impressionistic judgments.
Where appropriate, we instrument the production system to run evaluation continuously, turning a one-time assessment into an ongoing signal. This is particularly important for models that operate over long time horizons where drift is expected rather than exceptional.
What you receive
An evaluation report covering each assessed dimension with statistical findings, identified failure modes, severity assessment, and remediation recommendations. Where we design a continuous evaluation suite, deliverables include the test case library, metric definitions, and monitoring configuration — not just a report.
Who this is for
Organizations subject to model risk management requirements (SR 11-7, OCC guidance), regulated deployers of AI decision systems, teams that need independent evaluation evidence for procurement or audit, and organizations that have deployed models and need visibility into how they are performing in production.
Frequently asked questions
What is AI model evaluation?
AI model evaluation is independent assessment of how a model behaves across dimensions that functional testing does not capture: bias and fairness across demographic groups, robustness when input distributions shift, consistency across similar inputs, and performance degradation over time in production. It is distinct from vendor benchmarks, which measure capability under favorable conditions rather than failure modes under realistic ones.
How is model evaluation different from red-teaming?
Red-teaming tests adversarial behavior — what a motivated attacker can force a model to do. Model evaluation tests systemic behavior — how the model performs across its expected input distribution, including bias, fairness, and drift. Both are necessary. Red-teaming finds intentional misuse failures; evaluation finds deployment failures that emerge from the model's statistical properties.
What does a continuous evaluation suite produce?
A continuous evaluation suite is a set of test cases and metrics that run on a schedule or at deployment time, producing a dashboard of model behavior over time. It catches drift — the gradual shift in model outputs as the real-world input distribution diverges from training data — before that drift becomes an audit finding or a user-facing failure.