Evaluation & red teaming
Test task quality, unsupported outputs, prompt-injection scenarios, access boundaries and misuse relevant to the application. Record findings and retest mitigations.
Decide / Test the important questions before release
Responsible AI & assurance.
Evaluate AI systems against the tasks, risks and operating conditions that matter to your business. We help turn governance requirements into practical controls, tests and decisions throughout delivery.
Discuss this with usThe opportunity
Make trust part of how the system works.
A confident answer is not evidence that a system is correct or appropriate. Teams need to know where an application can fail, what it is allowed to do and who owns the decision to release or change it.
Begin with the intended use and consequences of failure. We map data and action permissions, define representative evaluations and test challenging behaviour. Findings become prioritised engineering work and explicit release decisions.
What we can do.
Governance & human control
Define system ownership, permitted uses, review thresholds, audit needs and change approval. Give teams practical operating guidance they can use.
Assurance evidence & risk review
Document tested scope, limitations and unresolved concerns. Support technical review and procurement discussions without presenting a test report as a blanket compliance certificate.
Tangible outputs
What you receive.
The exact scope is agreed around your workflow. A typical engagement can include:
- An intended-use and risk assessment
- Task and adversarial evaluation cases
- A prioritised findings and mitigation register
- Permissions, review and change-control guidance
- Evidence to support a release or procurement decision
Illustrative workflow
An assistant can update customer records
Define allowed actions → test permissions and misleading instructions → add approval points → retest → agree the release boundary.
An example of how this capability can fit a business process.Define what better looks like.
We agree useful measures before implementation and compare them with the current workflow.
- ↗Coverage of important failure scenarios
- ↗Resolution of material findings
- ↗Traceable decisions and control effectiveness
Your questions, answered.
Can you guarantee an AI system is safe?
No evaluation proves safety in every situation. We define the scope, test important risks and make remaining limitations and operating responsibilities clear.
Is governance only for large organisations?
No. The controls should match the use case and consequences. A simple internal drafting tool and an agent taking operational actions need different levels of oversight.
Do you provide legal compliance certification?
This service provides technical assessment and implementation evidence. Legal obligations and formal certification need the appropriate specialist review and agreed scope.
Useful before you start
Questions worth exploring.
AI agents or workflow automation?
Choose the simplest approach that can complete the work reliably. More autonomy is a design choice, not a success metric.
From AI pilot to production: what changes?
A working demonstration is a starting point. A released service also needs integration, ownership and a dependable response to failure.
A practical checklist for choosing an AI partner
Ask for evidence about delivery, integration and operation—not just a compelling demonstration.
A useful next step
Bring us the work
that should work better.
One process, one product idea, or a question about where to start.