Mid-cap Automotive Tier-1 Supplier
Cross-system defect-rate query: 3 weeks of ETL engineering → 47 seconds.
The challenge
A 1,200-table manufacturing schema spread across ERP, MES, QMS, PLM, and SCADA — five systems with no shared documentation, vendor-defined column names, and a decade of accumulated schema drift. Quality and supplier teams needed to compare defect rates across supplier batches by plant and component family — a question requiring joins across all five systems.
Existing approach: a 3-week ETL engineering project per question, with the resulting pipeline going stale at the next schema change.
The Antcluster solution
Antcluster ingested all five source databases into a unified Topology Graph over a one-time 4-hour ingestion. The Curve-Context Join Discovery algorithm validated cross-system join paths automatically, storing them as typed, confidence-weighted edges. The Middle Table pre-materialised high-confidence cross-source projections.
The defect-rate query was reformulated as a single natural-language question to the AntSwarm. The SQL Agent generated a multi-table join across the five systems; the ML Agent ran a correlation analysis; the Report Agent compiled the result with statistical significance markers.
Results
- Cross-system defect-rate query: 3 weeks of ETL → 47 seconds
- Join correctness on the 1,200-table schema: 61% (naive LLM baseline) → 96%
- Ad-hoc data-team requests reduced by 60% in the first 30 days
- New-analyst onboarding time to first correct query: under 1 day (was 3–6 months)
"We stopped writing ETL pipelines for every new question. Antcluster gave us the graph; we just ask." — Director of Manufacturing Analytics
Why this matters
The numbers above match the broader benchmark profile Antcluster publishes — anyone with a manufacturing schema of similar complexity should expect outcomes in this range. For a proof-of-concept on your own database, book a 30-minute demo.