Task performance.
Can the system complete the job? Evaluate correctness, groundedness, retrieval quality, and tool use against representative examples and agreed criteria.
Evals as a service
Confidence needs
a testable foundation.
Understand how your AI performs, where it fails, and what to fix next. Independent evaluations built around the work your system actually needs to do.
+ Quality in context
Test the promise.
Explore the limits.
An overall score tells only part of the story. We assess the behaviours that matter for your use case and investigate the failures behind them.
Safety & boundaries.
How does it behave under pressure? Probe prompt injection, data disclosure, unsafe tool use, and the points where a human should take over.
Change & regression.
Did the update make it better? Compare prompts, models, and system changes against a repeatable baseline, including latency and cost where relevant.
+ The engagement
Useful evidence.
An actionable next step.
Start with a focused assessment or agree a recurring evaluation programme. The scope follows your system and its risks.
Agree what good means.
Understand the system, users, and stakes. Define evaluation criteria and build or refine a representative test set with your team.
Run, inspect, reproduce.
Combine deterministic checks, model-based scoring, and human review where appropriate. Inspect failures rather than relying on aggregate scores.
Leave with a clear plan.
Get a findings report, reproducible failure examples, a versioned test suite, and prioritised recommendations for the next release.
Your stack. Your system. A fresh perspective.
Evaluations are available independently of our build services. We agree access, data handling, tools, and ownership before the engagement starts. Results are bounded by the agreed test scope and dataset.
+ Before we begin
Clarity at
every step.
I built my app with AI. Can you help get it production-ready?
Yes. We can start with your existing codebase, review the architecture and user experience, then identify security, reliability, and maintainability gaps. You get a clear scope before we begin the work of addressing them.
Can we work with you on evaluations alone?
Absolutely. Evals as a service is a standalone engagement. We can evaluate an existing agent or AI application, build a test baseline, and help your team understand and prioritise the findings.
What does “production-ready” mean in practice?
It means meeting release criteria agreed for your specific system: security controls, representative testing, a considered user experience, observability, recovery planning, and clear operational ownership. The criteria depend on the use case and risk; no system is risk-free.
How do you handle sensitive data and source code?
We agree access, confidentiality, and data-handling requirements before work starts. We scope permissions to the engagement and discuss approved tools, environments, and any model-provider data policies relevant to your project.
How are projects scoped and priced?
We start with a conversation about your starting point, goals, and constraints. We then propose a scope, deliverables, timeline, and commercial terms. For an unfamiliar codebase, an initial review can establish the right plan.
Let’s build with confidence.
Tell us about your idea or the system you want to improve.