Many Retrieval-Augmented Generation (RAG) pipelines fail because of inconsistent or noisy context. Tensorlake gives you fine-grained control by parsing documents into structured schema fields and clean, layout-aware chunks. This enables precise, filterable context for your retrieval system—no hallucinated blobs, no irrelevant footers.

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Why This Matters

  • LLMs hallucinate when given long, unstructured content
  • Chunking by paragraph or page often misses document semantics
  • Structured data helps ground LLMs in fact-based retrieval

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How Tensorlake Helps

  • Extracts schema-defined fields (e.g., coverage limits, terms, roles)
  • Produces clean text chunks with metadata (page #, section, bounding box)
  • Includes detection of tables, forms, and layout structure
  • Integrates with LlamaIndex, Weaviate, Pinecone, or your vector store of choice
Want help connecting Tensorlake output to your vector store? Join the Slack Community.