Parea AI

About Parea AI

Parea AI is an experimentation and human-annotation platform for teams shipping LLM applications, built around the questions that actually block a release: which samples regressed when I made this change, and does upgrading to a newer model improve performance or just move the failures around. It combines experiment tracking, evaluation, observability and human review in one place, with a feature that automatically drafts domain-specific evaluation functions rather than leaving a team to hand-write graders from scratch — usually the step where an evaluation practice stalls. Human review is treated as first-class: end users, subject-matter experts and product teams can comment on, annotate and label production logs, and those labels feed both QA and fine-tuning datasets. A prompt playground lets you tinker with several prompts on individual samples, test them across a large dataset, then deploy the winner. Observability covers staging and production logging with online evals, user-feedback capture and cost, latency and quality tracking. Logs can be promoted into test datasets, closing the loop between what happened in production and what the next experiment is measured against. Integration is via lightweight Python and JavaScript SDKs that wrap an existing OpenAI client and trace arbitrary functions with a decorator, so instrumenting an existing application is a handful of lines rather than a rewrite. The team also offers a separate AI consulting engagement for groups that want help designing an evaluation practice rather than only the tooling to run one.

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