Know what your AI is doing and what it costs. We set up tracing, evaluations and token cost tracking with LangSmith and Langfuse, so you can debug bad answers, catch regressions and keep spend predictable.
Why did our AI bill jump this week?
78% of the increase came from "summarize". A prompt change on Tuesday started sending full threads instead of the last 20 messages.
When an answer is wrong or the bill spikes, most teams have no record of what the model saw, what it did, or why.
The same discipline you apply to the rest of production, applied to AI
See every prompt, retrieval, tool call and response for any request.
Test prompt and model changes against real cases before they ship.
Token usage and spend by feature, model and customer.
Get notified about error spikes, slow responses and runaway spend.
We add tracing to your LLM calls, agents and RAG pipelines, tagged with feature, user and version.
We build datasets from real traffic and run evals on every prompt or model change, with automated and human scoring.
Dashboards and budgets per feature, plus the fixes that usually matter most: smaller models where they suffice, caching and tighter context.
Open and managed options, set up to fit your stack
Inventory your LLM calls, models and current spend.
Add tracing and metadata to every call path.
Stand up cost, latency and quality views.
Build test sets from real production traces.
Run evals automatically on prompt and model changes.
Apply routing, caching and prompt fixes, then measure the savings.
Included in every observability setup
Tell us how your AI features run today. We will set up tracing, evals and cost tracking so you can improve them with confidence.