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GitHub built Qubot, an internal Copilot-powered analytics agent that lets employees query their data warehouse in plain English via Slack, VS Code, or the CLI. It pulls federated context from raw, conformed, and curated data layers, then runs queries on Kusto or Trino to deliver SQL-backed insights within seconds. A continuous evaluation framework and community contributions to the context layer keep responses accurate and fast.
- Qubot lets any employee query GitHub's data warehouse in plain English via Slack, VS Code, or CLI, cutting analytics team question load and freeing analysts for harder work
- It uses a three-tier federated context layer (bronze/silver/gold) maintained collaboratively by product, analytics, and business teams, turning scattered knowledge into a single source of truth
- Investing in well-structured context both improved accuracy and cut answer time by two-thirds
- A continuous offline evaluation framework tests every context or agent update against known SQL answers to catch regressions before release
Encoding Your Domain Expert: The Context Layer Behind Spotify's Data Assistant | Spotify Engineering
Spotify built Vedder, an AI data assistant that uses domain-specific “clusters” curated by expert teams to provide reliable SQL-generated insights. Each cluster combines relevant datasets, vetted question–SQL pairs, and docs, with automated health scoring and feedback loops to keep context accurate and scalable.
- Auto-generating question-SQL examples from query logs mostly failed—only 12.5% passed expert review, proving curated human examples beat scraped ones
- Vedder's reliability comes from domain-expert-curated "clusters" (tables + vetted SQL examples + docs), not a bigger LLM
- Health scoring tracks schema drift and broken examples so experts know which clusters need fixing as data evolves
- Adopted by 2,100+ employees for 13,000+ conversations since August 2025, showing real traction beyond a pilot