AI Data Quality Assistant
Ask why your data failed — and get an answer you can trust.
01 / The business problem
Data quality alerts pile up. Teams know a rule failed but not why, what it affects or what to do next, and investigating each failure takes hours of specialist time.
02 / The capability
- 01
Natural-language questions over rules, metadata and failures
- 02
Explanations of anomalies, with the evidence behind them
- 03
Recommended corrective actions, approved by a person
- 04
Controlled queries and access rules over sensitive data
03 / The architecture
For technology and engineering teams
Retrieval over governed metadata, executable rules and lineage; model-agnostic (Gemini, Claude, OpenAI or open models); every answer cites its sources and is continuously evaluated.
- 01CatalogRules, metadata, lineage
- 02Quality runsResults and failures
- 03RetrievalGoverned RAG
- 04ReasoningLLM with guardrails
- 05AnswerWith cited sources
- 06ApprovalHuman in the loop
04 / What a pilot includes
- 1One data domain and its quality rules
- 2Assistant connected to your catalog and quality results
- 3An evaluation set built with your data stewards
- 4Access controls aligned with your security policies
Solution in active validation. Pilots and scope are defined with each client.