Six service lines that turn “it worked in the demo” into measurable, repeatable behaviour — from latency and throughput to failure modes, fallbacks, and audit-ready evidence.
Standard functional testing validates that your model produces the correct output — but it does not tell you whether it will still produce the correct output when 500 concurrent users hit it simultaneously, or whether response latency will breach your SLA thresholds during peak demand.
We measure latency distribution (P50, P95, P99), quality degradation under load, token efficiency, and throughput ceiling — benchmarked against UK sector-specific SLA expectations and your own production traffic patterns.
| Sector | P95 SLA | P99 SLA |
|---|---|---|
| Financial Services | < 2,000ms | < 5,000ms |
| Healthcare | < 1,500ms | < 3,000ms |
| Legal Services | < 3,000ms | < 8,000ms |
Your model may perform flawlessly in normal operation — but what happens when an adversarial user deliberately tries to manipulate it? Prompt injection, jailbreaks, data exfiltration attempts, and social engineering attacks are active threats for any publicly accessible LLM system.
Our red team testing is structured against the OWASP LLM Top 10, with sector-specific attack scenarios designed around the actual threat landscape for UK financial services, healthcare, and legal applications.
When your primary LLM provider goes down — and all major providers have experienced outages — does your system gracefully degrade, activate a fallback, and recover with no context loss? Or does it stall completely, leaving users with a non-responsive experience at exactly the wrong moment?
We run structured chaos engineering tests simulating provider outages, network degradation, and context window overflow. We validate your fallback chain, test recovery time objectives against your SLA, and verify compliant incident logging is in place.
Retrieval-augmented generation pipelines introduce a layer of risk that model testing alone cannot address. Poor retrieval quality, source attribution errors, context window mismanagement, and knowledge cutoff issues can all cause your RAG system to produce plausible-sounding but factually incorrect answers.
We run end-to-end RAG pipeline evaluations using RAGAS — measuring faithfulness, context precision, answer relevancy, and hallucination rate — with domain-specific ground-truth datasets across UK industry verticals.
| Metric | Score | Threshold |
|---|---|---|
| Faithfulness | 0.91 | ≥ 0.85 |
| Answer Relevancy | 0.87 | ≥ 0.80 |
| Context Precision | 0.73 | ≥ 0.80 |
| Context Recall | 0.89 | ≥ 0.85 |
| Hallucination Rate | 12.4% | ≤ 3% |
A one-time test engagement is not enough for a production LLM system. You need the ability to continuously monitor quality as your model, prompts, and data evolve. We design and build evaluation frameworks that your team can run and own independently — integrated into your CI/CD pipeline with full documentation and training.
Our frameworks combine automated LLM-as-judge scoring for high-throughput regression, human-in-the-loop review for critical edge cases, and MLflow-tracked experiment history that provides a defensible audit trail over time.
RAGAS + DeepEval — runs on every PR merge. Flags regressions automatically.
GPT-4o evaluator with calibrated rubrics. Runs on 10% sample + all flagged cases.
Domain expert review queue for high-risk outputs. Feeds calibration dataset.
MLflow tracks all runs, scores, and human decisions. Full audit trail history.
Most organisations approach governance as an afterthought — assembling evidence after the system is built, rather than building evidence generation into the testing process from the start. Retrofitting an audit trail for UK regulatory institutions is costly and often incomplete.
We work alongside your legal, compliance, and technical teams to produce the specific documentation, bias audits, risk classifications, and audit-ready evidence packs your regulator will expect to see.
| Framework | Coverage |
|---|---|
| OWASP LLM Top 10 | Full coverage |
| Performance & Load | Full coverage |
| Resilience & Red Team | Full coverage |
| RAG Pipeline | Full coverage |
| Bias & Fairness | Full coverage |
| UK Regulatory | Full coverage |
Tell us about your LLM deployment and we'll identify your highest-risk areas within two working days — at no cost, under NDA.