Blog
Highlights from the Currai blog: the posts worth reading first.
A complete, code-first guide to turning OpenAI Realtime voice conversations and text-to-speech generations into readable Currai User Stories and correctly nested traces.
A step-by-step guide to importing completed Retell AI calls into Currai with signed call-ended webhooks—without adding capture code to the live voice agent.
A step-by-step guide to importing completed Vapi calls into Currai with an authenticated webhook—without adding capture code or sharing a Vapi private API key.
Browse implementation notes, observability guides, product decisions, and workflow ideas by topic.
AI observability should do more than store traces. Currai turns traces into active signals for quality, cost, latency, prompts, tools, and evals.
Read moreHow AI chatbot pricing really works in 2026 — per seat, per message, per resolution, and usage models — and how to model your true cost before buying.
Read moreStateful agent evals need more than a final answer score. Currai ties agent steps, tool calls, sessions, cost, latency, and eval results back to the production trace.
Read moreAI evals are not just engineering tests. Product managers need real traces, domain judgment, and a repeatable loop for turning model failures into product improvements.
Read moreSummarization looks easy to grade and isn't — a fluent summary can be unfaithful, incomplete, or subtly wrong. Here's how to score the two things that actually matter: faithfulness to the source and coverage of what mattered.
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