Turn your proprietary knowledge into dynamic, queryable intelligence — giving your AI agents accurate, real-time access to what matters most.
What's Included
Semantic chunking strategies, embedding model selection, and vector store design tuned precisely for your content types and query patterns — maximizing retrieval precision from the ground up.
Hybrid search combining dense and sparse retrieval, cross-encoder re-ranking, and context compression techniques that deliver accurate, relevant results at speed — even across millions of documents.
Process and retrieve from text documents, PDFs, spreadsheets, code repositories, structured databases, and more — giving your agents a unified, comprehensive knowledge base across all your data formats.
How It Works
We inventory your data sources, assess quality and structure, and design ingestion pipelines that keep your knowledge base fresh and accurate as data changes over time.
We build and tune the full RAG pipeline — from chunking and embedding through retrieval and context assembly — validating accuracy against real queries from your domain.
Systematic evaluation using domain-specific test sets, with monitoring for retrieval quality drift and ongoing optimization as your data and query patterns evolve.
Ready?
Tell us about your knowledge base and we'll design a RAG architecture that gives your agents accurate, reliable access to it.
hello@polarite.ai