Problem statement
AI systems degrade over time without monitoring and governance, creating business, regulatory, and reputational risk.
audit
AI system audits for bias, performance, and explainability readiness.
We evaluate model behavior, data quality, governance controls, and production risk to support trustworthy AI operations.
What we do
- Assess model performance, drift exposure, and output consistency.
- Evaluate fairness, bias controls, and explainability practices.
- Review data lineage, prompt governance, and version discipline.
- Map findings to operational and compliance controls.
Process
- 1Model and workflow inventory
- 2Dataset and output evaluation
- 3Bias and explainability review
- 4Monitoring and governance gap analysis
- 5Control-aligned remediation plan
Tools & frameworks used
PythonMLflowEvidentlyWeights & BiasesGreat ExpectationsJupyter
Deliverables
- AI risk and governance report
- Model performance scorecards
- Bias and explainability findings
- Monitoring and policy improvement plan
Need a rapid technical baseline first?
We can run a focused service audit and return a concise execution plan with risk priorities, delivery phases, and control recommendations.