Evaluation suite
Real tasks, known failure cases, and pass criteria that each version of your AI product is tested against.
AI evaluation and operations
We build tests from the tasks your AI application handles and the mistakes users run into, so you can compare changes in accuracy, response time, and cost.
ScopeSpecific improvements or ongoing support for named systems
Delivery timingOngoing, on an agreed schedule
How we check itEvery change to a model or its instructions is tested on the same agreed tasks before release.
Find failures before your users do
Without repeatable tests, every model or prompt change is a guess. We run the tests before each release and turn new production errors into test cases, so you can see what a change improved or broke.
Good fit
- Teams moving an AI prototype into production
- Organizations with an AI feature they have no way to test
- Teams facing cost, speed, or reliability problems with an AI system
Not the right fit
- Logging model calls without measuring whether tasks succeed
- Systems no one on your team will own after the handoff
- Chasing benchmark scores that don’t reflect what users need
What you get
Live monitoring
Step-by-step records of live requests, with measures of answer quality, tool use, response time, and cost.
Release safeguards
Checks that catch changes that make results worse, plus fallbacks and recovery steps if behavior shifts after release.
How it works
Start from real failures
We review current outputs with your team and identify the failures that matter to users.
Turn failures into tests
We turn those failures and typical tasks into test cases covering search, generated answers, and actions.
Connect tests to releases
We run the tests on every change before it ships and review live failures afterward.
Scope, cost, and ownership
What we need from you
- Access to the product and representative inputs
- Reviewers who can judge whether a result is acceptable
What affects cost
- Number of features that depend on a model
- Human review and test-data needs
- Logging, release integration, and how serious failures would be
Technical scope
- integration
- Tracing of model and tool calls
- Offline and live-traffic evaluation
- Prompt and model versioning
- Cost controls
- Fallbacks when a model fails
Support and maintenance
We agree who reviews failed tests and production errors, updates the evaluation cases, and approves changes for release.
Common questions
Can you take over an existing system?
Yes. We review the existing application, inspect examples of errors, and check its logs and tests before recommending changes.
What should be evaluated?
The whole task first, then each step that depends on a model: retrieval, tool use, safety, , and cost. We test them with real cases.
Can you assess an application built with AI coding tools?
Yes. We review the software itself, not the tool that produced it: architecture, dependencies, user flows, permissions, data, evaluation, and deployment. You get a list of what to keep, repair, or rebuild. This engineering review does not replace a specialist penetration test or compliance audit.