Follow every request
Follow the request through application code, retrieval, models, tools, queues, and external systems.
See the whole request, not just the model response. In production, you need to see what actually happened.
See what your AI system is doing in production: prompts, answers, tool use, speed, cost, errors, and what users actually get.
- Designed for your stack
- Tested before release
- Monitored 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.
Add measurement points
Define trace boundaries, attributes, correlation IDs, redaction, and sampling.
Build useful views
Dashboards answer real operating questions instead of showing disconnected numbers.
Prove it works in production
Confirm live tracking data and metrics actually arrive, then rehearse failures and alerts.
Show us what you built.
Bring the current build, the workflow, or the production problem. We will map the shortest responsible path forward.