The problem
Data teams spend 60% of their time answering ad-hoc requests. Business stakeholders wait 2 days for a number that takes 5 minutes to compute. This kills trust in the data team and slows decisions.
Our approach
We build a natural-language interface over your warehouse. The agent translates questions into SQL/dbt runs, explains the results in plain English, flags data quality issues, and alerts when key metrics move anomalously.
Sample timeline
- 1
Week 1–2
Warehouse audit — catalogue tables, metrics, common query patterns
- 2
Week 3–5
Agent build — NL-to-SQL, dbt integration, result explanation
- 3
Week 6
Slack interface, anomaly detection, approval workflow for write operations
- 4
Week 7–8
Guardrail tuning, user testing, eval harness handoff