AI Engineering Assurance
Our AI Assurance Solutions
AI systems are non-deterministic — traditional testing paradigms don't apply. Our solutions provide structured, evidence-based assurance across all five AI system types, aligned with global regulatory frameworks.
Three Levels of AI Testing
Model-Level
Accuracy benchmarks, bias detection, and performance metrics at the algorithm level.
68% of orgs currently test at this level
System-Level
Integration testing, guardrail validation, and end-to-end pipeline assurance.
52% of orgs currently test at this level
User-Level
Human-in-the-loop evaluation with representative populations in real-world conditions.
27% of orgs currently test at this level
What we deliver
Solutions by AI System Type
Each AI system type requires a distinct testing strategy. We provide tailored frameworks for every category.
Generative AI Assurance
End-to-end testing for LLM-powered systems — hallucination detection, safety guardrail validation, and quality scoring using the WDR 0–2 rubric. Target <3% hallucination rate for high-stakes domains.
RAG AI Testing
Retrieval-Augmented Generation assurance covering context retrieval accuracy, grounding verification, and citation validation to ensure AI responses are factually anchored.
Agentic AI Testing
Comprehensive testing frameworks for autonomous AI agents — tool orchestration validation, multi-step reasoning verification, and failure scenario testing.
Predictive AI Assurance
Model accuracy benchmarking, drift detection, and continuous monitoring for predictive systems. Ensuring your ML models maintain performance in production.
Recommender AI Testing
Fairness metrics, bias detection across protected attributes, and user-level testing to ensure recommendation systems serve all populations equitably.
MCP (Model Context Protocol) Testing
Schema validation for tool responses, authentication testing, tool failure scenarios, and concurrent invocation testing for MCP-integrated systems.
AI Red Teaming
Methodical adversarial testing using jailbreak prompts, misleading queries, and stress conditions to identify failure modes and vulnerabilities before production deployment.
Regulatory Compliance & Traceability
Full traceability from test cases through risk categories to EU AI Act, NIST AI RMF, and ISO 42001/23894 requirements — creating the evidence chain regulators demand.
WDR AI Risk Taxonomy
15 Categories of AI Failure
Our assurance approach maps to the WDR AI Risk Taxonomy — enabling traceability from specific test cases through risk categories to NIST Trustworthiness Characteristics.
Scoring Rubric
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Book a FREE AI Assurance Consultation
Let us assess your AI systems against the WDR framework and build a tailored assurance roadmap.