Services / Data & AI
AI Evaluation & Safety
Test AI features rigorously before they ship — and monitor them after.
Overview
What this involves
AI features tested rigorously before they ship — and monitored after. We build evaluation frameworks, guardrails, and fallback mechanisms that make AI features reliable in production, not just impressive in demos. Our safety practice covers output validation, bias detection, cost monitoring, and ongoing evaluation against representative test sets.
Deliverables
What you get
- Evaluation framework and test suite
- Guardrail and content filter configuration
- Production monitoring and alerting
- Ongoing evaluation and reporting cadence
Questions
Frequently asked
- What does AI safety mean in practice?
- It means ensuring AI features behave reliably and do not produce harmful, incorrect, or unexpected outputs. In practice, this involves content filtering, output validation, rate limiting, cost controls, and comprehensive testing against edge cases and adversarial inputs.
- How do you monitor AI features after deployment?
- We instrument AI features to track accuracy, latency, cost, and user feedback. We set up alerts for anomalies and establish a regular review cadence to evaluate performance against your test suite. AI features need ongoing attention — they do not stay static.