DataVersion - AI Document Intelligence
50,000+ technical documents processed with 99.2% accuracy
Visit DataVersion AI50,000+
Documents Processed
99.2%
Answer Accuracy
< 3 seconds
Response Time
Tesla, Kawasaki, Lucid Motors
Clients Include
The Problem
DataVersion turns technical manuals, SOPs, datasheets, and engineering drawings into a searchable AI knowledge base. Engineering teams were spending 3-5 hours daily digging through documentation. They needed a RAG pipeline that could handle OCR, table extraction, and complex technical formats while citing exact pages and sections.
Our Approach
Built the document processing pipeline with FastAPI handling ingestion, OCR, and chunking. Pinecone as the vector store for embeddings. Supabase for metadata and user management. Next.js frontend with a chat interface. Deployed on AWS with auto-scaling for enterprise workloads. The key challenge was handling technical formats like CAD references, spec tables, and scanned PDFs accurately.
Pipeline Breakdown
01 · Collect
- Document upload handling (PDF, DOCX, XLSX, scanned images, engineering drawings)
- OCR processing with layout and table structure preservation
- CAD reference, spec table, and diagram extraction
- Incremental sync: only processes new or changed documents
02 · Process
- Technical-domain chunking strategy preserving specification context
- Embedding pipeline and Pinecone vector store for semantic retrieval
- Hybrid search combining dense vectors with BM25 for spec queries
- Cross-encoder reranking for precision on ambiguous engineering queries
03 · Act
- Chat interface with instant answers to technical queries
- Exact source document, page, and section citations on every response
- Shared knowledge base accessible across engineering teams
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