Blog
Highlights from the Currai blog: the posts worth reading first.
Use real production conversations to find agent failures, make focused prompt changes, build better evals, and verify that user outcomes improve.
Monitor customer success agents with end-to-end traces, conversation outcomes, groundedness, escalation quality, policy compliance, latency, and cost.
A beginner-friendly, code-first guide to turning Vapi browser calls into Currai sessions, conversation User Stories, and correctly nested voice-agent traces.
Browse implementation notes, observability guides, product decisions, and workflow ideas by topic.
A RAG answer that takes four seconds could be slow retrieval, a fat prompt, or the model itself. Nested traces tell you which one — here's how to find the bottleneck.
Read morePass token usage on every generation and Currai turns it into cost — rolled up per trace, model, user, and day. Stop guessing what an LLM feature costs.
Read moreOne conversation is many traces. Pass a session id to stitch every turn into one thread, and a user id to slice cost, latency, and volume by the people using your app.
Read morePoint an existing Langfuse SDK at Currai, validate trace parity in a canary, and cut over with a tested rollback path.
Read moreTotal latency hides the metric users actually feel — time to first token. Here's how to capture both on every generation and find what's making your LLM app feel slow.
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