Our services
AI Assurance Services
Traditional testing fails for AI systems. Non-deterministic outputs, emergent behaviours, and evolving models demand a new assurance paradigm — and that's exactly what we deliver.
<3%
Hallucination rate target for high-stakes domains
0%
Safety violation rate target
<2%
Unexplained bias disparity target
<1%
Guardrail bypass rate target
<5%
Refusal error rate target
6
Proven testing frameworks
AI Red Teaming & Adversarial Testing
Independent adversarial testing using jailbreak prompts, misleading queries, and stress conditions. We identify failure modes and vulnerabilities before your users do — aligned with NIST AI RMF guidance that red teaming should be composed of external experts independent from internal AI actors.
Bias & Fairness Testing
Quantifying bias across protected attributes at model, system, and user levels. Our fairness metrics suite tests across multiple demographic dimensions — because only 43% of organisations conduct any fairness testing today.
Hallucination Detection & Content Safety
Systematic detection of confabulation, grounding verification, and safety guardrail validation. Targeting <3% hallucination rate for high-stakes domains with 0% safety violation tolerance.
Continuous Drift Monitoring
Production monitoring for model drift and performance degradation. Automated alerting when AI systems deviate from baseline quality thresholds — ensuring your models maintain accuracy over time.
Regulatory Compliance Mapping
End-to-end traceability from your test cases through risk categories to EU AI Act, NIST AI Risk Management Framework, and ISO 42001/23894 requirements. Building the evidence chain that regulators increasingly demand.
AI Quality Strategy & Consulting
Establishing dedicated AI Quality functions within your organisation. We help build teams with expertise in both traditional QA and AI-specific testing methodologies — from strategy through to implementation.
User-Level Testing (Human-in-the-Loop)
Evaluating AI systems in real-world conditions with representative users. Does the user understand what the AI is doing? Are responses culturally appropriate? Do users trust the system enough to act?
AI Testing Framework Implementation
Deploying our six proven testing frameworks across Generative AI, RAG, Agentic AI, Predictive AI, Recommender systems, and MCP-integrated platforms — each with structured testing workflows and key metrics.
From the WDR Framework
Key Recommendations
- 1
Establish a dedicated AI Quality function led by practitioners with expertise in both traditional QA and AI-specific testing methodologies.
- 2
Invest in red teaming and adversarial testing capabilities — mandate red teaming before any production deployment.
- 3
Measure and monitor bias across protected attributes at model, system, and user levels.
- 4
Implement continuous monitoring and drift detection for all AI systems in production.
- 5
Map your AI testing programme to regulatory frameworks (EU AI Act, NIST RMF, ISO 42001/23894).
- 6
Build user-level testing into your lifecycle through human-in-the-loop evaluation with representative populations.
Ready to assure your AI?
Start with a FREE Consultation
We'll assess your AI systems, identify risk areas, and recommend a tailored assurance programme.