Our current approach: - Sandboxed / sample runs on smaller datasets before full execution - Step-level transparency: summaries, intermediate tables, and generated code are all visible - Parallel and sequential test-time execution to surface inconsistencies - dbt-style pipelines for reproducibility and explicit dependencies - Decomposing analyses into small, verifiable steps to avoid error compounding (similar to MAKER-style approaches) - Online validation checks on intermediate and final outputs that trigger re-analysis when assumptions are violated - A gradually evolving semantic layer to improve consistency and governance over time
Curious how others think about this: what would make you trust an AI-driven data platform?