AI Governance & Risk

Evaluation & monitoring

Testing for accuracy, bias, robustness and drift, with dashboards and alerts for production systems.

Vue d’ensemble

Governance only works if you can show that AI systems perform as intended, both at launch and over time. Evaluation and monitoring provide that evidence, turning policy commitments into measurable checks.

Before deployment, we design evaluation suites tailored to each system's purpose and risk: accuracy and task success on representative data, fairness across relevant groups, robustness to unusual or adversarial inputs and, for generative AI, groundedness, safety and adherence to policy. We combine automated metrics, model-based grading and structured human review, with thresholds agreed with business owners and risk teams.

In production, we set up monitoring for performance, drift, data quality, safety incidents and cost, with dashboards for owners and alerts routed to the people who can act. Results feed periodic reviews and governance reporting, so leadership and auditors can see how AI systems are performing and what was done when issues arose.

Ce que vous obtenez

01Evaluation strategy and metrics for each AI system
02Test datasets and automated evaluation suites
03Bias, robustness and safety testing results
04Production monitoring dashboards and alerting
05Periodic performance reports for governance forums

Technologies que nous maîtrisons

MLflow
Evidently
Fairlearn
Arize Phoenix
LangSmith
Langfuse
Ragas
Grafana

Nous sommes indépendants des éditeurs : les outils sont choisis en fonction de vos besoins et de vos investissements existants.

Les questions que l’on nous pose souvent

How do you evaluate generative AI when there is no single right answer?

We define what a good response looks like for each task, build evaluation sets from real examples and combine automated checks, model-based grading and expert human review. Scores are tracked over time so changes in quality are visible.

How often should AI systems be reviewed?

Monitoring should be continuous, with formal reviews scheduled according to risk tier. Higher-risk systems are typically reviewed more often and after any significant change to the model, data or use.