Golden datasets from real traffic
We sample your production traces, cluster them by intent, and label the ones that matter — including the failures. The result is a test set that looks like your users, not a benchmark.
Know your AI works before your customers find out.
Eval suites, tracing, cost controls, and release gates for the LLM features you already run. Catch regressions in CI, not in support tickets — and swap models without a rewrite.
For LLM features already in production
Six controls for the AI you already run.
Start with the one that hurts most. Each works with your current stack and providers.
Graders you can audit
Exact match where answers are fixed. Rubrics where they are not. LLM judges only after they agree with your experts on a labelled sample.
Exact matchRubricLLM judgeHuman check
Tracing on every request
Prompt version, model, retrieved context, tool calls, tokens, latency, and cost on one trace. Search by user, feature, or failure.
OpenTelemetryVersionsPII redaction
Evals as CI gates
Every prompt, model, or retrieval change runs the suite against the current baseline. Scores are compared per slice, so a gain on average cannot hide a drop for one customer segment. Below the floor, the merge is blocked.
Cost and latency budgets
A cost-per-request and p95 latency budget for each feature. Dashboards show where you stand. Alerts fire before the invoice or the user does.
Model routing
One layer between your code and the providers. Set the model, fallback, and limits per feature in config. Switch providers by changing a line and rerunning the evals.
Observability
See every request, end to end.
When an answer goes wrong, you need the full path: which prompt version, which documents, which tool calls, how long each took, and what it cost. We instrument once, and every feature reports the same way.
- OpenTelemetry traces, sent to the backend you already run or a dedicated LLM tracing tool.
- Prompt and model versions on every span, so a regression maps to a change.
- Cost per request, per feature, and per customer in your dashboards.
- Personal data redacted before a trace leaves your app.
Safety
Red-team it before users do.
Prompt injection, data leaks, and off-policy answers belong in the test suite, not the incident log. We build an adversarial set for your feature and run it on every release.
- Injection attempts through user input, retrieved documents, and tool output.
- Leak checks for system prompts, other users' data, and secrets.
- Policy checks for topics your legal team rules out.
- Each finding is tracked to a fix, then kept as a permanent regression test.
Every change
Prompt and model changes gated on evals in CI
Per feature
Cost and latency budgets, with alerts
1 file
Sets each feature's model, fallback, and limits
Yours
Datasets, graders, and dashboards live in your accounts
We used to hear about bad prompt changes from support tickets. Now the pull request fails first.
Questions
What buyers ask us first.
Our LLM features are already live. Where do you start?
With tracing and a first golden set built from your real traffic. That shows where quality, cost, or latency is slipping. The CI gate comes next.
Do we have to change providers or frameworks?
No. We instrument what you run today. The routing layer is optional, and lets you add or swap providers later with a config change.
Who labels the golden dataset?
We draft labels and graders. Your domain experts review a sample and settle disagreements. LLM judges are trusted only after they match those human labels.
Which tools do you use?
Open-source eval frameworks and OpenTelemetry where they fit, or the tracing platform you already pay for. Everything lives in your repo and your accounts.
How is it priced?
Each engagement is scoped and priced after a discovery call. Model and tooling usage bills to your own accounts.
Ready when you are
Show us the AI feature you're nervous about.
A 30-minute discovery call. We'll tell you what to measure first.