Tensorlake @tensorlake.ai · 05/11/2025Document parsing benchmarks have been measuring the wrong thing. We tested every major parser on real enterprise documents. The results will change how you think about OCR accuracy 🧵 122
Tensorlake @tensorlake.ai · 23/10/2025Want to build scalable data lakes w/ Tensorlake + @qdrant.bsky.social? In the free Qdrant Essentials Course, learn how to: - Architect vector-powered data lakes - Optimize ETL pipelines - Create knowledge graphs - Integrate @langchain.bsky.social agents for natural language queries t.co/OoPZswrL7z 012
Tensorlake @tensorlake.ai · 10/10/2025Most parsers strip all tracked changes when you extract the text. That means: ❌ Lost audit trails ❌ Manual review of revision history ❌ No programmatic access to reviewer comments ❌ Workflows that can't route based on specific edits 100
Tensorlake @tensorlake.ai · 02/10/2025OCR engines constantly mess up document hierarchy. Section 2.2 becomes a top-level header (##) instead of nested (###). We just shipped automatic header correction. 🧵 How it works: 110
Tensorlake @tensorlake.ai · 19/09/2025Step 3: Generating AI responses with verifiable citations Once your chunks carry anchors, retrieval doesn’t change. You can use the dense, hybrid, or reranker setup you already have. Consider hiding the anchors in prose, while keeping them in output and making IDs clickable. 100
Tensorlake @tensorlake.ai · 19/09/2025Step 2: Create contextualized chunks Iterate through page fragment objects and create appropriately sized chunks by combining them. As you create the chunks, you can create contextualized metadata to help during retrieval. 100
Tensorlake @tensorlake.ai · 19/09/2025Step 1: Parse docs with bounding boxes Using our Document AI API you get a full document layout. For each page fragment you have access to the page number, fragment type, content, and bounding box. Making it easy to add metadata and anchor points to chunks before embedding. 100
Tensorlake @tensorlake.ai · 11/09/2025In finance, clinical trials, or performance benchmarks, dense tables contain mission-critical data. But flatten that data like most parsers do and trust is lost. Tensorlake restores trust by preserving structure, generating summaries for effective embeddings, and attaching evidence via b-boxes. 111
Tensorlake @tensorlake.ai · 05/09/2025You can now login into Tensorlake using Microsoft and Azure SSO credentials. This is the beginning of better integration with Microsoft Azure and Tensorlake. If you are using Azure, and need better Document Ingestion and ETL for unstructured data reach out to us! 020
Tensorlake @tensorlake.ai · 05/09/2025To build trustworthy AI, your data needs proof. Get citations for every field extracted with Tensorlake. Read the blog and try our citations with the example notebooks: tlake.link/blog/citations 000
Tensorlake @tensorlake.ai · 21/08/2025Step 4: Test your context-aware agents This is the fun part, use Tensorlake to extract key claims from news articles, then use your @langchain.bsky.social agent to query ChromaDB and determine whether the claims are rooted in fact. 100
Tensorlake @tensorlake.ai · 21/08/2025Step 3: Contextualize Queries Don't rely on users to make queries that are specific for your data. Instead, make sure you contextualize your query so that during hybrid search you're finding the most relevant and accurate chunks. In our example, we used @langchain.bsky.social to help 100
Tensorlake @tensorlake.ai · 21/08/2025Step 2: Chunk, Store, and Retrieve Data With clean, structured, and accurate data you can chunk and embed your documents in a way that is effectively and accurately retrievable by agents. In our example, we used @chonkieai.bsky.social and ChromaDB. 100
Tensorlake @tensorlake.ai · 21/08/2025Step 1: Ingest and pre-process your documents With a single API call, you can turn messy PDFs into page-aware, table-preserving, structured context. Simply: 1. Define page classes for different documents 2. Define schemas to extract relevant data 3. Parse 100
Tensorlake @tensorlake.ai · 21/08/2025“RAG is dead” is lazy. What’s dead is cosine‑N without a retrieval plan. We ship advanced RAG...out of the box: • Classify pages → target sections • Extract structured fields → filter by form_type, fiscal_period • Verify data; cite page/bbox Want to know how? 🧵👇 110
Tensorlake @tensorlake.ai · 14/07/2025Most "unstructured" parses fail on when layout gets tricky: multiple columns, fragmented text blocks, mixed reading order Tensorlake doesn't. ✅ Authors parsed as one clean chunk ✅ Abstract follows, exactly as it should Unstructured ≠ unordered Preserve reading order. Parse with Tensorlake. 010
Tensorlake @tensorlake.ai · 16/05/2025🚀 We’re in the top 3 on Product Hunt today just 6.5 hours after launch! Huge thanks to everyone supporting Tensorlake 🎉 From devs wrangling PDFs to teams automating high-stakes workflows. If you haven’t yet, check us out 👇 110
Tensorlake @tensorlake.ai · 16/05/2025We just launched Tensorlake Cloud on Product Hunt 🎉 If you’ve dealt with messy document workflows and trying to parse complex documents (insurance claims, financial docs, multi-page forms), this is for you. Would love your support 💚 www.producthunt.com/products/te... 031
