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AI Evaluation and Monitoring
KNOW HOWOFTEN ITIS WRONG
We build the tests and monitoring that tell you whether an AI feature is good enough to release and whether it stays that way, using evaluation sets drawn from your real cases.
AI Evaluation and MonitoringWhat we build
If you cannot say how often an AI feature is wrong, and in what way, you are not ready to change it or to trust it.
Knowing how good your AI really is
- 01Evaluation datasets
- 02Failure taxonomy
- 03Live monitoring
AI evaluation measures the quality of a model-driven feature against cases that represent real use, and monitoring keeps measuring once it is live. We build the dataset, define what correct means with your experts, classify the ways the system fails, automate the scoring and connect it to your release process and production logs.
It suits teams with an assistant, agent or extraction feature that seems to work and cannot prove it, and teams afraid to change a prompt or model because nobody knows what would break. Evaluation is not a one-time audit. It becomes part of how the product is developed, in the way automated tests are for ordinary software.
Talk to us about your projectCapabilities, End to End
What It Looks Like When It Ships
From Kickoff to Launch
With the people who own the outcome we write down what a correct, acceptable and unacceptable result looks like for each task, using real examples to settle disagreements.
We gather and label representative cases, check that experts agree with one another on the labels and set aside a portion that is never used for tuning.
Scorers are implemented and compared with human ratings until they agree closely enough to trust, then wired into the development workflow so that every change produces a report.
Sampling, dashboards and alerts are switched on for live traffic, a review routine is agreed with your team and the first baseline report sets the figures future releases must beat.
Built for Teams Like Yours
What buyers ask before they start
If yours is not here, ask us directly. The first conversation is free and useful whether or not we work together.
Book a callFewer than most teams fear to start, and more as the product matures. A small, carefully chosen set that covers the main tasks and known problem cases already catches most regressions. It grows with every confirmed failure from production. Coverage matters more than size, so we check the set against the real mix of requests instead of chasing a headline number.
For many criteria, yes, provided the grader is checked. We write a clear rubric, have people rate a sample, and compare the grader's scores with theirs. Where agreement is high, the grader can run at scale. Where it is not, we refine the rubric or keep a human in that part of the loop. An unvalidated grader gives confident figures that mean little.
Quality on sampled outputs, the rate of refusals and escalations, response time, cost per request, tool errors and user feedback. We also watch the inputs, since a shift in what people ask often explains a fall in quality before any code changes. Alerts are set on trends, not on single bad answers, so the team is warned without being flooded.
Yes. Evaluation is largely independent of how a system was built. We need access to its inputs and outputs, ideally to logs and prompts, and time with the people who can judge correctness. An outside evaluation is often useful precisely because the builders are too close to the product to see its common failures clearly.
It turns a risky decision into a measured one. With a dataset and scorers in place, a new model or a cheaper one can be run against the same cases and compared on quality, speed and cost within a day. Without them, a switch is judged by a few manual tries, and regressions surface later through users. Providers retire models regularly, so the question always returns.
