RAG Implementation
Build a custom AI knowledge base from your documents, databases, and internal data. Get accurate, sourced answers.
What's Included
Vector Database Setup
Pinecone, Weaviate, Qdrant, or pgvector configured for your data
Document Ingestion Pipeline
Automated processing of PDFs, docs, emails, and knowledge bases
Embedding Optimization
Custom chunking, embedding model selection, and metadata strategy
Retrieval Engine
Hybrid search with semantic + keyword matching for 99%+ accuracy
LLM Integration
GPT-4 or Claude connected to your knowledge base with citations
Production Deployment
Scalable infrastructure with caching, rate limiting, and monitoring
How It Works
Week 1
Data Audit — We catalog your knowledge sources, assess data quality, and design the ingestion pipeline.
Week 2
Infrastructure — We set up the vector database, embedding pipeline, and retrieval system.
Week 3
Fine-Tuning — We optimize chunking strategy, test retrieval accuracy, and tune the LLM prompts.
Week 4
Integration — We connect the RAG system to your application, add monitoring, and deploy to production.
Use Cases
Internal knowledge base Q&A for employees. Customer support with answers grounded in your documentation. Legal document search and clause retrieval. Medical literature research assistant. Product documentation chatbot. Sales enablement with proposal generation from past deals.
Ideal For
Companies with large document repositories who want AI answers grounded in their own data — not hallucinated responses. Teams that need accurate, cited answers from internal knowledge.
Ready to Get Started?
Book a free strategy call to discuss your needs, or purchase now and we'll kick off within 48 hours.
