AI Data & Model Intelligence
AI Safety Evaluation
Responsible deployment requires understanding how a system fails. We help teams probe models for harmful, biased or policy-violating behavior and document findings for remediation.
Challenges
What we help solve
- Unknown failure modes before launch
- Policies that are hard to test consistently
- Limited internal red-teaming capacity
- Need for documented evaluation evidence
Capabilities
Adversarial testing and safety review for AI systems.
Red teaming
Structured adversarial prompting against agreed risk categories.
Policy evaluation
Testing model behavior against your content and use policies.
Bias review
Evaluation of outputs across demographic and linguistic variations.
Reviewer wellbeing
Exposure limits and support practices for sensitive content work.
Delivery approach
How the work runs
- 1Discover
- 2Define
- 3Design
- 4Build
- 5Validate
- 6Deliver
- 7Improve
Deliverables
What you receive
- Risk taxonomy and test plan
- Adversarial prompt sets
- Findings and remediation report