Retrieval-Augmented Systems
Knowledge bases that actually know things. Grounded answers with citations.
Overview
Our RAG systems provide accurate, cited answers from your proprietary documents. Built for enterprise search with traceable provenance, semantic understanding, and intelligent chunking strategies that preserve context.
Core Capabilities
Multi-Format Ingestion
PDFs, Word docs, Excel, PowerPoint, HTML, markdown, and databases
Intelligent Chunking
Context-aware splitting that preserves semantic boundaries
Hybrid Search
Combines semantic similarity with keyword matching for best results
Citation Tracking
Every answer includes source documents with page numbers
Access Control
Document-level permissions with user authentication
Continuous Learning
Feedback loops to improve retrieval quality over time
Technical Stack
Implementation Process
Document Analysis
Audit your knowledge base and identify retrieval requirements
Pipeline Design
Select optimal chunking, embedding, and retrieval strategies
Implementation
Build ingestion pipeline, vector store, and query interface
Evaluation
Test retrieval quality with synthetic queries and edge cases
Deployment
Production rollout with monitoring and feedback collection
Use Cases
- Technical documentation search for engineering teams
- Legal contract analysis and compliance checking
- Customer support knowledge base with instant answers
- Parts catalog search with specifications
- Research paper exploration for R&D teams
- Policy and procedure lookup for HR
Turn your documents into answers
Deploy a RAG system that actually understands your content.
Request a Strategy Session