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 plain-language explanation of RAG — how retrieval grounds an LLM in your data, why it's the default architecture for factual AI apps, and the two places it quietly breaks that you have to evaluate.
Read moreWhat customer service automation is, which processes to automate, how AI changes it in 2026, and how to automate support without hurting quality.
Read moreNew to evaluating LLM apps? Start here. What evaluation is, why 'it looks good' isn't a strategy, and the smallest useful loop you can stand up this week — no framework, no jargon.
Read moreAn honest look at AI customer support in 2026 — what genuinely works, why many deployments fail, and how to build one that customers actually trust.
Read moreHow AI delivers multilingual customer support across dozens of languages, what breaks in translation, and how to keep answers accurate in every language.
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