Independent assurance for modern software and AI-enabled services. We test how systems behave under load, during failure and recovery, and against the quality and governance expectations that matter in production.
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.
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.
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.
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.
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 —.
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.
Tell us about your LLM deployment and we'll identify your highest-risk areas within two working days — at no cost, under NDA.