AI validation
AI systems do not fail like traditional software.
They fail through drift, ambiguity, weak prompts, missing guardrails, bad retrieval, inconsistent responses, hidden data paths, and business logic gaps.
Behavioral validation
Test against user intent, business expectations, edge cases, refusal boundaries, consistency, tone, confidence, and recovery behavior.
Prompt and response scenario matrices
Expected behavior libraries
Negative path and ambiguity testing
Data-path validation
Verify what information the AI can access, how it retrieves it, and whether output is grounded, stale, incomplete, or unsafe.
Source grounding checks
Retrieval quality validation
Payload, metadata, and permission-aware testing
Governance and risk control
Connect product behavior to compliance, operational risk, auditability, and explainability.
Risk taxonomy
Validation evidence
Audit-ready test summaries
Release readiness
Report behavioral risk, known gaps, regression exposure, and recommended release posture.
Go/no-go signal
Production monitoring inputs
Post-release learning loop