GenAI-Powered Technical Documentation Assistant

Overview

A global industrial equipment manufacturer had accumulated decades of technical documentation across hundreds of product lines. Field technicians struggled to find relevant information quickly, leading to extended service times and frequent escalations.

The Challenge

The documentation problem had grown beyond manual solutions:

• •10,000+ pages of technical manuals, service bulletins, and troubleshooting guides
• •Multiple formats including PDFs, legacy systems, and tribal knowledge
• •Time pressure with technicians needing answers in minutes, not hours
• •Complex equipment requiring precise technical guidance
• •Global teams requiring multilingual support

"Our technicians spent more time searching for information than actually fixing equipment. Knowledge was there, but finding it was the bottleneck."

Our Solution

1. Document Processing Pipeline

Built multimodal ingestion system:

• •PDF extraction with layout preservation
• •Table and diagram recognition
• •OCR for scanned documents
• •Metadata extraction and indexing

2. RAG Architecture

Implemented Retrieval-Augmented Generation:

Query → Hybrid Retrieval → Context Assembly → LLM → Response
          ↓
    Dense Embeddings + BM25 Keyword Search
          ↓
    Semantic Chunking with Overlap

3. Semantic Chunking

Preserved document structure:

• •Section-aware splitting
• •Table integrity maintenance
• •Cross-reference handling
• •Hierarchy preservation

4. Hybrid Retrieval

Combined approaches for accuracy:

5. Conversational Interface

Deployed user-friendly chat:

• •Natural language queries
• •Follow-up conversation support
• •Citation links to source documents
• •Confidence indicators

Technical Stack

LLM: OpenAI GPT-4
Embeddings: text-embedding-3-large
Vector DB: Pinecone
Framework: LangChain
Backend: FastAPI, Python
Frontend: React
Cloud: Azure

Multilingual Support

Enabled global deployment:

• •On-the-fly translation for queries
• •Source language preservation
• •Support for 8 key languages
• •Localized terminology handling

Results

The system transformed field service operations:

• •70% faster information retrieval
• •45% reduction in escalated service calls
• •25% decrease in average repair time
• •90% satisfaction score from technicians
• •5,000+ queries/day handled across global teams

Sample Interactions

Query: "How to replace the hydraulic pump seal on Model XR-500?"

Response: Step-by-step procedure with:

• •Required tools and parts
• •Safety precautions
• •Torque specifications
• •Diagrams from service manual
• •Related service bulletins

Key Takeaways

1. •Document quality and structure significantly impact RAG performance
2. •Hybrid retrieval outperforms pure semantic search for technical content
3. •Citations and source links build user trust
4. •Continuous feedback improves retrieval accuracy
5. •Multilingual support is essential for global enterprises