Document Intelligence Engine
Ask questions, get answers from thousands of documents
Engineers and analysts spent hours digging through PDFs, spreadsheets, and wikis to find specific information. Tribal knowledge lived in documents nobody could find.
The client had thousands of documents across multiple systems—technical specs in SharePoint, procedures in Confluence, data in spreadsheets. Finding the answer to a specific question meant opening dozens of files and Ctrl+F-ing through each one. New hires took months to learn where information lived.
RAG with Source Attribution
Vector search over chunked documents, with LLM synthesis of retrieved passages. Every answer links back to the exact source paragraph for verification.
Hybrid Search
Combines embedding similarity with keyword matching. Catches both conceptual matches and exact terms (like part numbers or acronyms) that pure semantic search misses.
Multi-Hop Retrieval
For questions that span multiple documents, the system chains retrievals—finds the relevant spec, then pulls the related procedure, then checks the changelog.
Multi-Format Ingestion
Parsing pipeline handles PDFs, Word docs, Excel files, and Confluence pages. Extracts tables and images, not just text.
Search time dropped from 30+ minutes to under 30 seconds for most queries.
New engineers onboard faster—they can ask questions instead of hunting through folders.
Every answer includes clickable citations to source documents.
Works across 1000+ documents without manual tagging or organization.
Reduced dependency on senior staff for institutional knowledge.
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