How Schneider Electric Built Their LLMOps Foundations At Enterprise Scale With LangSmith

Schneider Electric’s AI Hub, with 350 experts, has deployed 60+ agents across energy, forecasting, and customer success, all built on LangSmith. The platform is self‑hosted on AWS EKS behind the corporate perimeter, with one workspace per AI product that spans dev, QA, pre‑prod, and prod. Offline evaluation is accelerated with a LangSmith SDK, and an LLMOps maturity framework tracks instrumentation, evaluation, online evals, and SME feedback. Production traces are automatically reused to create regression datasets, enabling continuous improvement and drift detection. The Customer Success Manager Copilot uses LangSmith for annotation queues, and the deployment architecture follows LangSmith Deployment reference architecture, giving each product its own Agent Server with Postgres and Redis. This per‑product runtime model avoids a single point of failure and gives teams full control over latency, cost, and incident response. The LLMOps loop—observability, evaluation, deployment—provides a structured path from prototype to production for 140,000 employees in 100+ countries.

Schneider’s case study demonstrates how a large enterprise can build a mature LLMOps practice around LangSmith, achieving reliable, traceable, and continuously improving AI assistants at scale.