AI That Knows Your Data.

RAG (Retrieval-Augmented Generation) combines a retrieval system with an LLM. When a user asks a question, the system first searches your knowledge base for relevant documents, then passes those documents to the LLM as context to generate an accurate, cited answer grounded in your actual data.

Fine-tuning trains the model on your data, which is expensive, slow to update, and can cause the model to forget general knowledge. RAG keeps the model as-is and retrieves relevant context at query time. RAG is 60% cheaper, updates instantly when you add new documents, and maintains full model capabilities.

Our RAG systems handle PDFs, Word documents, HTML pages, Markdown, Confluence wikis, Notion databases, Google Docs, Slack messages, email archives, code repositories, and structured data from databases. We build custom parsers for any document format.

We use advanced chunking strategies, hybrid search (semantic + keyword), cross-encoder re-ranking, and comprehensive evaluation pipelines. Our systems are tested with automated quality metrics (Ragas, DeepEval) and human evaluation to achieve 99%+ retrieval accuracy.

Yes. We architect RAG systems using distributed vector databases (Pinecone, Weaviate, Qdrant) that scale to billions of vectors. Combined with efficient indexing, caching, and query optimization, our systems maintain sub-200ms latency even with millions of documents.

A basic RAG chatbot starts at $15,000. Enterprise knowledge base systems with multi-source ingestion, RBAC, and custom UI start at $38,000. Ongoing infrastructure costs (vector DB, LLM API) start at $150/month depending on volume.