AI document intelligence cut automotive engineering analysis time by 70% across CAD drawings and supplier specs.
- Document analysis time
- -70%
- Procurement prep
- Faster
- Knowledge unification
- Single layer
What we shipped
A computer vision and NLP platform that auto-structures engineering drawings, specs, and supplier documents into a unified knowledge layer. Engineers and procurement teams retrieve components, materials, and compliance requirements from one interface.
Automotive manufacturers handle enormous volumes of technical documentation across engineering, compliance, procurement, and production. CAD drawings, component specifications, supplier documents, and manufacturing plans are scattered across multiple internal systems. Engineers and procurement teams spend hours manually reviewing technical files to identify components, materials, and compliance requirements. Without a unified knowledge layer, technical decisions lose institutional context and supplier coordination suffers.
Blueprint → AI Pilot → Production launch → Scale and operate.
We followed the Datablooz Delivery Model. See our process.
- Blueprint
Audited engineering documentation sources, supplier repositories, and compliance stores. Defined target entities (components, materials, standards) and downstream consumers.
- AI Pilot
Trained computer vision models on engineering drawings and NLP models on specifications, validating extraction accuracy on a pilot vehicle program.
- Production launch
Deployed document intelligence pipelines alongside a knowledge graph connecting parts, suppliers, and standards, with RAG-based search across engineering data.
- Scale and operate
Extended ingestion to new documentation types, tracked extraction quality, and rolled out unified search across engineering and procurement teams.
Business, technical, and governance outcomes.
- Up to 70% less time spent analyzing engineering documents.
- Faster procurement preparation for new vehicle programs.
- Improved compliance validation across technical files.
- Unified engineering knowledge layer across teams.
- Python
- Computer Vision
- NLP
- Knowledge Graphs
- RAG
- Vector Database
Structured metadata, auditable extraction pipelines, and access-controlled retrieval across engineering and supplier documentation.
Working on something similar?
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Reference calls available under NDA after the second working session.