Learn how OpenAI's Data Productivity team built Kepler, an internal AI data agent serving 3,500+ employees, on OpenMetadata as its open context layer. They turned days-long data hunts into self-service answers in under 90 seconds. Inside this case study:

  • How OpenMetadata grounds every answer in governed metadata (schemas, lineage, query history) so the agent finds the right table by meaning rather than keyword match
  • The six-layer context model and compounding memory that cut repeat queries from 22 minutes to under 90 seconds
  • How Kepler enforces permissions at every layer, from retrieval to chain-of-thought, across 70,000 datasets and 580+ petabytes a day