Automated evals score every response against your own metrics. Guardrail testing makes sure the agent doesn't leak data or give in to misuse.
Response quality is scored against your business metrics instead of gut feeling or random spot checks.
Automated evals for every response: relevance, groundedness, tone and your own business metrics.
A dataset built from real conversations that every change can be run against.
Every prompt and model change is verified with evals before it reaches production.
Systematic testing for misuse and data leakage before real users can trigger them.
Iteration based on measured results. You see how each change affects quality scores before release.
Get notified the moment quality drops or a guardrail fires in production.
Every eval and test is logged to the same dashboard. You see how many requests the agent has handled, what happened along the way and how many of them have been scored, day by day.
Evals, guardrail tests and user feedback are logged to the same dashboard, right next to your traces.
See how usage and quality scores change day by day, and whether your latest change improved quality.
Spikes and dips stand out in the chart, and you can drill straight down into individual traces.
What the example shows: the mid-October usage spike produced almost no quality scores because automated evals weren't running yet. By the late-October spike, nearly every trace was being scored.
Not everything can or should be measured the same way. We combine three types of evals, depending on what each criterion needs and how much an evaluation may cost.
Checks written as code for things that are clear-cut: is the response in the right format, did the agent call the right tool with the right parameters, and does the answer stay within agreed limits.
They are fast, nearly free and always reproducible, which is why they run on every response.
A language model grades the agent's response against a written rubric: is it relevant, is it grounded in the sources, and does it follow the tone and instructions.
Ideal for criteria you can't write down as a rule. We calibrate the judge against human ratings so you can trust the scores.
When the agent makes decisions (approving, rejecting, classifying or routing), we compare its outcome with a decision model that defines the correct result, such as a decision table or a rules engine.
You see how often the agent reaches the same outcome as the agreed process, and in which cases it deviates.
A phased delivery where every phase has a clear outcome.
Together we agree on what a good answer means for your business.
The dataset is built from real conversations and edge cases.
We automate the evals and run the attack scenarios end to end.
Scoring runs in production and alerts you to anomalies.
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