... Just dropped a new integration for developers working with real-world documents and vector search: Tensorlake + Qdrant = structure-aware retrieval at scale. Instead of flat blobs, you can: Parse PDFs into labeled fields extracting metadata like names, dates, balances, etc Embed with semantic structure Store and query with Qdrant filters that actually match your use case Built for RAG, document agents, contract intelligence, and anyone tired of lossy parsing. Full blog: https://www.tensorlake.ai/blog/a... Open to feedback or ideas ...

... What s New We just launched a native integration between LangChain and Tensorlake! Now you can pass unstructured documents to a LangGraph agent and trust that parsing, chunking, and field-level accuracy are handled by Tensorlake

... Signatures might feel like a formality until they delay a claim, break compliance, or derail a deal. That s why we built Signature Detection into Tensorlake, giving you the power to track and act on signature presence inside your documents: Basic Detection Detects whether any signature is present Returns bounding box coordinates and presence/absence flags Contextual Detection Associates signatures with names and roles (e.g., buyer, seller, agent) Extracts structured fields like signer_name and signature_date Works alongside your schemas ...