Break large documents into perfectly sized, semantically coherent chunks that preserve meaning while fitting AI model limits. Every piece is enriched with metadata for precision retrieval.
AI models have token limits, but your documents don't respect those boundaries. Our intelligent chunking system breaks content into optimal sizes while preserving the meaning and context that makes answers useful.
Chopping documents arbitrarily destroys context. A sentence split mid-thought provides useless answers. Our chunking algorithm respects semantic boundaries:
The result: every chunk makes sense on its own and provides complete, useful information to customers.
Different content types get specialized handling:
Chunks overlap slightly at boundaries, ensuring critical information isn't lost at the edges where one chunk ends and another begins. This overlap is optimized to balance completeness with storage efficiency.
Each chunk is enriched with comprehensive metadata:
When a chunk is retrieved, customers see not just the answer but WHERE it came from: "Installation Guide > Chapter 3 > Database Setup" instead of a generic citation.
Chunks are validated for minimum coherence, maximum size constraints, encoding correctness, and metadata completeness. Chunks failing validation are flagged for manual review rather than being added to your knowledge base, protecting answer quality.