TURN YOUR DATA INTO AN INTELLIGENT KNOWLEDGE SYSTEM
Build retrieval-augmented generation systems that give accurate, grounded answers from your enterprise data. No hallucinations, full traceability.
Why RAG?
- Ground LLM responses in your actual data
- Reduce hallucinations by 80%+ with proper retrieval
- Keep sensitive data on-premise or in your cloud
- Scale to millions of documents without retraining
- Real-time updates: no model fine-tuning needed
- Audit trail: know exactly which sources informed each answer
What I Deliver
RAG Architecture Design
Design retrieval pipelines optimized for your use case. Chunking strategies, embedding models, reranking, and hybrid search.
Vector Database Setup
Implementation with Qdrant, Weaviate, Pinecone, or pgvector. Indexing optimization, metadata filtering, and scaling strategies.
Production Deployment
End-to-end implementation with monitoring, evaluation pipelines, and continuous improvement systems.
Use Cases
Internal Knowledge Base
Let employees query company documents, policies, and procedures in natural language.
Customer Support
Build AI assistants that answer questions using your product documentation and support history.
Legal & Compliance
Search across contracts, regulations, and legal documents with precise citations.
Research & Analysis
Query scientific papers, reports, and research data to surface relevant insights.
Common Questions
What's the difference between RAG and fine-tuning?
Fine-tuning changes the model itself. RAG keeps the model unchanged but gives it access to your data at query time. RAG is better for: data that changes frequently, when you need source citations, and when you can't share data with model providers.
How long does a typical RAG implementation take?
A production-ready RAG system typically takes 4-8 weeks. This includes: architecture design, data pipeline setup, vector database configuration, LLM integration, evaluation framework, and production deployment.
What LLMs do you work with?
OpenAI (GPT-4), Anthropic (Claude), open-source models (Llama, Mistral), and Azure/AWS hosted options. The choice depends on your latency, cost, and data privacy requirements.
Can RAG work with non-English documents?
Yes. Modern embedding models support 100+ languages. I've implemented multilingual RAG systems for clients with documents in Spanish, German, French, and more.