Braintrust builds an AI observability platform for measuring, evaluating, and improving AI systems in production. The platform integrates LLM evaluation into standard engineering workflows, serving companies including Notion, Stripe, Zapier, Vercel, and Ramp. The system enables teams to iterate on AI applications through real-time data pipelines that convert production data into evaluation feedback, with interfaces designed for both engineering iteration and product prototyping.
The technical architecture centers on evaluation tooling that supports daily feature deployment cadence. The platform provides UI-based prototyping for non-engineers and real-time review workflows for cross-functional teams. Core infrastructure runs on Go, Python, and Node.js, with Postgres and Redis for data persistence and caching, deployed on AWS via Terraform and Docker.
The team operates as a small group focused on developer tooling problems: building data pipelines for production AI systems, creating evaluation interfaces for LLM performance measurement, and developing workflows that reduce latency in feedback loops. Technical domains span AI development, model evaluation frameworks, real-time data infrastructure, and engineering workflow optimization.