Engineering Knowledge Assistant

The Challenge

A Tier 1 automotive supplier with 12 manufacturing facilities faced a critical knowledge management problem. Their engineering teams spent an estimated 8-12 hours per week searching for technical documentation across multiple systems:

• PLM system: CAD files, engineering drawings, BOMs
• Document management: Specifications, procedures, standards
• MES: Process parameters, work instructions
• Legacy file shares: Tribal knowledge, historical data
• Email archives: Supplier communications, change requests

Engineers frequently couldn't find what they needed, leading to duplicate work, inconsistent decisions, and delayed launches. The problem was exacerbated by retiring senior engineers taking institutional knowledge with them.

"Our engineers were spending more time searching for information than using it. We had the data, but no one could find it when they needed it."

— Director of Engineering

The Solution

We deployed a domain-specific AI assistant designed specifically for automotive engineering workflows. The system combined:

Multi-Source Knowledge Integration

We built connectors to ingest documents from all their systems into a unified semantic index. The system processes:

• PDFs, Word documents, Excel spreadsheets
• CAD file metadata and associated specifications
• Structured data from PLM and MES systems
• Email threads with technical discussions

Domain-Specific Understanding

Generic search doesn't understand that "GD&T" means geometric dimensioning and tolerancing, or that "PPAP" relates to production part approval. We fine-tuned embedding models and response generation on automotive engineering terminology.

flowchart TB
    subgraph Sources[Data Sources]
        PLM[PLM System]
        DMS[Document Management]
        MES[MES System]
        Email[Email Archives]
        Files[File Shares]
    end
    
    subgraph Processing[AI Processing]
        Ingest[Document Ingestion]
        Chunk[Smart Chunking]
        Embed[Domain Embeddings]
        Index[Vector Index]
    end
    
    subgraph Query[Query Pipeline]
        Question[Engineer Question]
        Search[Semantic Search]
        Rerank[Cross-Encoder Rerank]
        Generate[Domain-Tuned Response]
    end
    
    PLM --> Ingest
    DMS --> Ingest
    MES --> Ingest
    Email --> Ingest
    Files --> Ingest
    
    Ingest --> Chunk --> Embed --> Index
    
    Question --> Search
    Index --> Search --> Rerank --> Generate
                

Quality Gates

Every response includes:

• Source citations with document names and page numbers
• Confidence scoring to flag uncertain answers
• Escalation paths when the system can't find relevant information

Implementation

Results

Within 90 days of deployment:

• 73% reduction in time spent searching for documentation
• 89% accuracy on engineering questions (verified by SMEs)
• 15,000+ documents indexed and searchable via natural language
• 2,400+ queries/month processed after full rollout

"The system actually understands what we're asking for. I asked about torque specs for a specific assembly and it pulled the exact document section I needed - something that would have taken me 30 minutes to find manually."

— Senior Manufacturing Engineer

Technical Details