CASE STUDY
Parts Knowledge RAG System
Turning 10,000+ technical documents into instant, accurate answers
The Challenge
An industrial equipment distributor with over 50,000 SKUs was drowning in support tickets. Their technicians spent hours searching through PDFs, datasheets, and manuals to answer customer questions about compatibility, specifications, and troubleshooting. Knowledge was siloed, and new hires took months to become productive.
The Solution
Intelligent Document Processing
- • Ingested 10,000+ technical documents (PDFs, specs, manuals)
- • Smart chunking preserving tables and diagrams
- • Multi-modal understanding of text and images
- • Automatic metadata extraction (part numbers, specs)
Retrieval & Answer Generation
- • Hybrid search combining semantic and keyword matching
- • Every answer includes source citations with page numbers
- • Confidence scoring to flag uncertain responses
- • Feedback loop for continuous improvement
Implementation Details
Document Processing: Unstructured.io
Embeddings: OpenAI text-embedding-3-large
Vector Store: Pinecone (10M vectors)
Reranking: Cohere rerank-english-v3.0
LLM: GPT-4-turbo for answer synthesis
Frontend: React chat interface
Results
73%
First-contact resolution
4min
Average query response
92%
Answer accuracy rate
Support ticket volume decreased by 65% as technicians found answers instantly. New hires became productive in days instead of months. The system now serves as the single source of truth for all technical knowledge.
Example Interactions
Query:
“What's the torque spec for mounting a BR30 actuator?”
Response:
The mounting torque specification for BR30 actuator is 45-50 ft-lbs.
Source: BR30 Installation Manual, Page 27, Section 4.3
Query:
“Can I use a VFD-2000 with 480V 3-phase?”
Response:
Yes, the VFD-2000 supports 480V 3-phase input (380-500V range). Ensure proper grounding and use recommended fuses (30A, Class J).
Source: VFD-2000 Datasheet, Page 3; Installation Guide, Page 12
Key Features
- Natural language queries - no special syntax required
- Multi-document answers with citation tracking
- Understands technical diagrams and schematics
- Handles cross-references between documents
- Learns from user feedback and corrections
- Export answers with full source documentation
Client Testimonial
“This RAG system has transformed how we handle technical support. Our team spends less time searching and more time solving customer problems. The citation feature gives us confidence in every answer we provide.”
Turn your documents into answers
Let's build a knowledge system that actually knows.
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