How we built an AI-powered RAG system that reduced document search time by 90% for a telecommunications team managing thousands of technical specifications.
A leading telecommunications team specializing in large-scale construction projects (oil & gas, infrastructure) was drowning in documentation. With thousands of technical specifications, product datasheets, and compliance documents stored across SharePoint, engineers spent hours manually searching for the right equipment specifications. Critical project timelines were delayed as teams struggled to find explosion-proof radios, temperature-rated equipment, or ATEX-certified devices matching specific environmental requirements.
We developed a comprehensive RAG (Retrieval-Augmented Generation) system that transforms SharePoint document libraries into an intelligent knowledge base. The system extracts text from PDFs, DOCX, and XLSX files, creates vector embeddings using OpenAI's ada-002 model, and stores them in Pinecone for semantic search. Advanced specification extraction identifies temperature ranges, pressure ratings, certifications, and application types, enabling intelligent filtering beyond simple keyword matching.
AI-powered semantic search across thousands of documents
Intelligent specification extraction (temperature, pressure, certifications)
Hybrid search combining vector similarity and metadata filtering
Real-time SharePoint synchronization
Source citations with direct document links
Engineers can now find the exact equipment specifications in seconds instead of hours. The system handles complex queries like 'explosion-proof radios for oil platforms operating at -20°C to +60°C with ATEX certification' and returns precise, cited results. Project timelines improved significantly, and the team can focus on engineering rather than document hunting.
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