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Solution 06

AI Assurance

Proof that the AI works — before and after you trust it.

06Solution 06

Proof that the AI works — before and after you trust it.

Anyone can put a chatbot in front of a customer. Knowing whether it is right, watching what it does at three in the morning, and finding out how it breaks before somebody else does — that is a different discipline. We do this on systems we built, and on systems built by someone else entirely.

Who it's forFor banks, insurers, telcos, health providers and public bodies putting AI in front of customers — and for anyone who has been sold an AI system and wants an independent read on whether it works.

How we test it

  • AI evaluation

    Does it actually do the job? We build a test set from your real cases and measure the system against it, so "it seems good" becomes a number you can put in a board pack.

    • 01Test sets built from your own cases, not public benchmarks
    • 02Accuracy, refusal, hallucination and escalation rates
    • 03Comparison between models, vendors and prompts before you commit

  • AI observability & monitoring

    What did it do last night? Live visibility into every conversation, cost, failure and escalation, with alerts when behaviour drifts away from what you signed off.

    • Tracing of every request, tool call and response
    • Cost, latency and failure dashboards
    • Drift and quality alerts before customers notice

  • AI red teaming & security

    We attack the system the way a motivated person would — prompt injection, data extraction, policy bypass, abuse of tools it can call — and hand you the findings with fixes, not just a scary list.

    • Prompt injection and jailbreak testing against your live rules
    • Data-leak testing: what it will say that it should not
    • Abuse testing on the actions and tools the agent can trigger

  • Governance & compliance readiness

    The documentation, controls and human-oversight rules a regulator, a partner or an enterprise client will ask you for — written before they ask.

    • 01Human-in-the-loop rules for decisions AI must never make alone
    • 02Data handling, retention and residency documentation
    • 03An audit trail that stands up to being audited

What the report says

  • A written evaluation report with numbers, not adjectives.
  • A monitoring setup that tells you about a problem before your customers do.
  • A red-team report: what we broke, how, how bad it is, and how to fix it.
  • Human-oversight rules written down and agreed, not assumed.
  • A re-test after the fixes, so you know the fixes worked.

Not sure which one you need?

Tell us what is eating your team's week. We will tell you which of these fixes it, and roughly what it costs. If the honest answer is that you should fix something else first, you will hear that too.