Know what every AI feature costs, and when its answers get worse.
We trace the AI features you run in production, build evals from your real runs, and measure what each model and reasoning setting costs against the quality it delivers. A check on every release catches it when either one slips. We run AI agents in production ourselves, so we built this for our own product first.
Tracing, evals, and a check on every release.
We set it up in your own accounts, on the tracing tool you already use or on Langfuse in your own environment, and we keep it current as your product changes.
Tracing
Every step of every AI feature, visible: the prompt, the context it retrieved, the tools it called, what it cost and how long it took. You see the cost per feature, and when a customer reports a bad answer, you can see why it happened.
Evals from real runs
Built from your own production runs, with an agreed definition, per feature, of what a wrong answer is. They measure quality and cost side by side, so every choice of model or reasoning effort for a feature is a measured trade-off. Every new failure that shows up in production is added.
A check on every release
Every model, prompt or code change runs against the evals before it ships, and quality and cost in production are watched between releases. A regression shows up in your pipeline, before it reaches a customer.
The part nobody keeps up.
Coding agents make it quicker than ever to add an AI feature. Each one brings prompts, models and tools that change on their own schedule. Any of those changes can make answers worse without failing a single test, and the choice of model and reasoning effort is made once and rarely revisited, because nobody measured what another setting would cost or deliver. Wiring up tracing takes a week. Keeping the evals honest across every change is the work that slips.
Start with one feature.
Pick the AI feature that costs or worries you most. You see what it costs, where it goes wrong today, and how cost and quality move as you change model or reasoning effort. If your AI does not need this yet, we will tell you.
The first feature
We trace the feature, build the first evals from your real runs, and walk your team through where the answers go wrong, how often, on which kinds of input, and what each run costs.
After that
We add the release check, extend it to your other AI features, and keep the evals current as your product changes. It all runs in your own tooling, so your engineers can take it over whenever they want.
We also design and build agent systems: browser automation, search over large document sets, and chat agents that act on your behalf. Our tools show what that looks like.
Which AI feature worries you?
Tell us what your AI does, what it costs you, and where it has gone wrong before. An engineer will reply.