Core AI Governance Services
The controls that turn a model you hope behaves into one you can prove behaved. Most teams add this after an incident. It's far cheaper before.
Responsible AI
Responsible AI isn't a policy document. It's the checks that catch a biased or harmful outcome before it reaches a user. We test your models and the data behind them for the failure modes that turn into complaints, lawsuits, and headlines, while they're still fixable.
- Bias and fairness testing across the groups your model affects
- Data privacy and consent checked at the source
- Harmful-output testing before the model reaches real users
Model governance
You can't govern what you can't see. We build the record of which model version is live, what data trained it, who approved it, and what it's been doing. So when someone asks how a decision was made, you have an answer instead of a shrug.
- Model registry with version, lineage, and approval trail
- Access and change controls, so no model ships unreviewed
- Audit-ready records for regulators and internal review
Explainable AI
"The model said so" is not an answer a regulator accepts. Explainable AI gives you the why behind a prediction, in terms a person can follow. It's the difference between defending a decision and being unable to. We build it in where the stakes, or the rules, demand it.
- Prediction-level explanations a non-technical reviewer can read
- Explanations tied to the features that actually drove the decision
- Documentation that stands up in an audit or a dispute
