Possibility 01
Test a customer assistant against common questions and difficult escalation cases.
Ongoing operation & improvement
AI testing, evaluations and performance reviews
Check how an AI system behaves against representative tasks, known failure cases and the purpose it is meant to serve.
Discuss an AI performance reviewCheck how the system behaves in practice.
Define good behaviour
Select representative tasks, difficult cases and measurable review criteria.
Run the evaluation
Inspect answers, tool actions, escalation, latency and usage where relevant.
Prioritise improvements
Document failure patterns and compare changes against the same baseline.
Explore each step. The approach is shaped around your tools, people and review needs.
See how it could workThe work behind the idea
Check how the system behaves in practice.
A convincing demonstration may not reveal how an AI system handles difficult questions or unexpected inputs. Evaluation creates a repeatable way to examine quality, failures and the effect of a proposed change.
Possibility 02
Compare a new model or prompt against the current setup before changing production behaviour.
Illustrative use cases, ready to adapt to your business. These are not claims of completed client work.
From question to a workable approach
How this could
come together.
- 01
Define good behaviour: select representative tasks, difficult cases and measurable review criteria.
- 02
Run the evaluation: inspect answers, tool actions, escalation, latency and usage where relevant.
- 03
Prioritise improvements: document failure patterns and compare changes against the same baseline.
Define it together
A useful scope.
A clear starting point.
Available to discuss and scope. The right approach depends on your systems, information, people and the decisions you want to support.
What we can scope
- Define evaluation cases, quality criteria and review procedures.
- Assess answers, tool actions, escalation and cost; recommend focused improvements.
What helps us get started
- The system’s intended use and current configuration
- Representative test cases with expected behaviour
- Permitted access to outputs, traces or operating measurements
Worth thinking through
Passing a test set does not prove a system is safe in every situation. Evaluation coverage, data permissions and the consequences of missed failures should be explicit.
Let’s talk about your next step
How well does your AI handle the difficult cases?
Share the system’s intended use and the behaviour you want to check. We can scope repeatable evaluations around realistic tasks, failures and human review.
Discuss an AI performance review