Turn a promising AI agent into a dependable one.

At a glance

The performance questions we answer

• Is the agent completing the right task, not merely producing a plausible response?
• Where are hallucinations, tool failures, poor retrieval or weak prompts entering the journey?
• Which model and architecture give the required quality at a sustainable cost?
• When should the system ask, refuse, escalate or hand control to a person?
• How will quality be monitored after each model, prompt or workflow change?

At a glance: Baseline evaluation → Failure analysis → Targeted improvement → Controlled release → Continuous monitoring

What this changes for you

Better performance across the production equation

• Higher task success: Evaluate against the work the agent must complete.
• Lower failure risk: Reduce unsupported answers and make escalation behaviour explicit.
• Faster responses: Find avoidable retrieval, orchestration and model latency.
• Controlled cost: Match model, context and token use to the value of each task.

What you receive

• Evaluation strategy and production-quality scorecard
• Representative test set and scenario library
• Conversation, prompt and workflow audit
• Retrieval and grounding assessment where RAG is used
• Tool-call and integration failure analysis
• Model comparison and routing recommendations
• Latency and token-cost analysis
• Guardrail, fallback and human-escalation design
• Observability requirements and regression dashboard
• Prioritised improvement backlog
• Optimised prompts, flows or system changes within agreed scope

How we improve an AI system

1. Define success by task: Establish correct outcomes, tolerances and unacceptable failure modes.
2. Build an evaluation set: Cover typical, difficult, ambiguous, adversarial and interrupted journeys.
3. Trace failure sources: Separate model issues from prompt, retrieval, memory, tools, data and UX problems.
4. Optimise the system: Test changes to prompts, models, context, routing, retrieval and orchestration.
5. Release with controls: Compare against the baseline, monitor regressions and retain human intervention where needed.

Integration examples

Evaluation and observability can cover permitted model endpoints, retrieval systems, vector stores, databases and connected business tools. Integration and implementation depend on the current architecture, available interfaces, client permissions, security controls and technical feasibility.