End-to-end traces
Follow the request through application code, retrieval, models, tools, queues, and external systems.
See the whole request, not just themodel response.Production behavior needs a trace you can use.
End-to-end observability for AI systems across prompts, retrieval, tool calls, model behavior, latency, cost, errors, and user outcomes.
- Designed for your stack
- Evaluated before release
- Observable in production
- Owned by your team
What production-ready means here
You get a working system with explicit quality boundaries — not a model call wrapped in a polished interface.
How we take it from uncertain to dependable
We start with the behavior and operating constraints that matter, then make each release measurable and reversible.
Instrument the path
Define trace boundaries, attributes, correlation IDs, redaction, and sampling.
Build useful views
Dashboards answer operating questions instead of displaying disconnected telemetry.
Prove it works in production
Confirm live traces and metrics actually arrive, then rehearse the failure and alert paths.
Show us what you built.
Bring the current build, the workflow, or the production problem. We will map the shortest responsible path forward.