Enables cleaning clinical-research tables by mapping fields, classifying and normalizing cells, routing confidence-gated decisions to a review queue, and exporting reports and audit logs through an MCP stdio interface.
Enables AI-driven data quality operations such as validation, enrichment, and deduplication for customer data fields including email, address, phone, and LinkedIn profiles through natural language interactions.
Enables autonomous data quality inspection and repair workflows. It scans DuckDB warehouses for anomalies, generates and verifies fixes in a dry-run copy, then applies them after validation, with full audit logging.
Enables AI assistants to load datasets and receive structured data-quality findings, including column classifications, missing-data summaries, and ranked issues for exploratory data analysis.
Enables data-quality workflows by applying named, deterministic transformations to records and generating replayable lineage with content hashes, so users can verify that cleaned results follow exactly from raw inputs.
Enables a language model to run four deterministic data checks it cannot argue with — column-contract validation with an explicit quarantine list, arithmetic reconciliation of opening/movements/closing, distinct entity counting against row counts, and duplicate detection returned for human review — each answering with structured findings and row/column locators. Failures come back as results with reasons, so the model reports exactly which rows and columns are broken rather than asserting the data is clean.
