Quality Engineering for AI

Specialized checks spanning hallucination detection, prompt robustness, jailbreak resistance, RAG faithfulness scoring, multi-turn consistency, and agentic AI tool-use validation. EvalSuite goes where conventional test suites cannot.

- 11 structured tests (model + GenAI)
- Tiered: deterministic → LLM-as-Judge → human
- Agentic AI and multi-step reasoning

Schema validation, bias audits, data drift monitoring, PII compliance testing, and RAG-specific retrieval quality checks. DataSentinel ensures the foundation every AI system depends on is sound - before and after deployment.

- Five structured tests
- RAGAS context precision and recall
- EU AI Act data governance mapping

Alignment to NIST AI RMF, ISO/IEC 42001, and the EU AI Act. TrustScore covers red-teaming, fairness and demographic-parity testing, disparate-impact analysis, and explainability validation, and generates the full evidence artifact set that auditors require.

- 11 structured tests (fairness + safety + governance)
- Red-teaming as a recurring control
- EU AI Act technical documentation

Latency benchmarking (TTFT, ITL, P95/P99), throughput and scalability testing, cost-per-inference optimization, resilience and chaos engineering, and security penetration testing, including prompt injection, model extraction, and supply chain review.

- Three structured tests
- vLLM, GenAI-Perf, Locust, Garak
- OWASP LLM Top 10 coverage

Agentic AI systems, where models are equipped with tools and tasked with autonomous multi-step reasoning, require testing approaches that scripted regression suites cannot provide. AgentProbe validates tool-use authorization boundaries, reasoning-chain coherence, graceful degradation when tool calls fail, and instruction-following fidelity across complex, multi-constraint task specifications.

It explicitly tests whether systems respect their intended scope under adversarial inputs designed to expand authority, a form of prompt injection specific to agentic contexts. Coverage spans both white-box (full model access) and black-box (API-only) attack scenarios.

TrustScore maps the assurance program to the four functions of the NIST AI RMF - govern, map, measure, and manage - and generates concrete evidence artifacts: governance policies, system context mapping, quantitative testing evidence, and incident response plans. It produces the control matrix, risk register, red-team report, and post-market monitoring plan that auditors and regulators expect.

Red-teaming is treated as a recurring control and not a one-time launch exercise. Safety testing covers direct and indirect prompt injection, multilingual adversarial prompts, jailbreak families, and confabulation induction. Fairness testing extends to intersectional subgroup analysis across combined protected attributes.

DataSentinel provides continuous validation across five data quality dimensions, including completeness, consistency, accuracy, timeliness, and bias representation. For RAG systems specifically, it measures context precision and context recall to ensure the retrieval component is surfacing the right passages for generation.

Data lineage and provenance verification create an end-to-end audit trail from data origin through model inference, a requirement increasingly mandated by the EU AI Act. Drift detection monitors shifts in the statistical distributions of training and production data, triggering investigations or retraining when thresholds are exceeded.