Why every data quality tool tells you what broke — but leaves you alone to figure out why

Short summary

Most data quality tools report which validation checks failed but don't explain why, forcing engineers into manual debugging that can take hours. Aegis DQ addresses this by adding an LLM-powered diagnosis layer that analyzes failures, identifies likely root causes, and proposes SQL fixes—all for ~$0.005 per run using Claude Haiku. The framework automatically generates validation rules from database schemas and business policies, includes audit logging, and supports local execution via Ollama.

• •Existing tools (Great Expectations, Soda Core, dbt) report failures but leave engineers to debug root causes manually
• •Aegis DQ adds LLM diagnosis layer to suggest causes and SQL fixes, costing ~$0.005 per run with Claude Haiku
• •Automatic rule generation from schemas and policies; full audit trail; local execution option available

Generated with AI, which can make mistakes.