Announcing Tensorlake Cloud
Up-leveling Document Ingestion and Workflows for building agentic applications and complex business workflows.
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Tensorlake
346 posts
Tensorlake
@tensorlake
Scalable Sandbox and Orchestration Infrastructure for Agents
- We’ve heard this question from many of you: “Which PDF extraction model is best for my documents?” Well, we’ve got you covered! 📊 We benchmarked a few popular PDF extractors to give you insights on the best models for scientific documents and books. Models Compared: •
- We are super excited to finally announce @tensorlake's open-source, real-time data framework, Indexify. It fits into any LLM stack and provides a foundational building block for bringing your data to LLMs. Read the Blog post: medium.com/tensorlake-ai/… Code:github.com/tensorlakeai/i…
- Processing complex invoices into JSON for RAG applications or agents to understand expenses and take actions? Here is an end to end example that uses an open source PDF extractor capable of understanding complex layouts with images and tables, and transforming them into JSON
- It’s time to keep up with modern RAG. Stop stuffing entire PDFs into your vector DB. With Tensorlake + @qdrant_engine, you can: - Parse and extract only the useful parts of a doc - Index precise segments like tables or specific sections - Run focused, context-aware search
- Stop Feeding RAG Garbage. Most RAG pipelines fail because they’re chunking noisy, flattened PDFs. Garbage in = garbage out. A 🧵
- Document 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 🧵
- HuggingFace has a model for every possible AI task under the sun. Indexify makes it easy to build fault-tolerant and continuously running pipelines with any @huggingface model! 🚀 @RishirajAcharya wrote a blog about using 🤗 with Indexify!
- We 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 💚 (link in thread)
- Structured Extraction from images power a lot of real world Agentic use cases, such as validation of license plates, driving licenses, information from invoices captured by images. Our Document Ingestion API allows you to extract data from millions of images without spinning up
- Most RAG workflows fail because your data is messy. 👎 PDFs mix tables, forms, and text in unpredictable ways. 👎 Layouts break chunking logic. 👎 Relevant pages hide in noise. Tensorlake gives you context engineering out-of-the-box: ✅ Page classification to skip
- 21 hours later and we’re in the top 5 on Product Hunt! 🚀 Huge thanks to everyone who supported, upvoted, and shared 💚 Tensorlake is just getting started. Stay tuned - there’s so much more to come. P.S. There's still time to upvote our launch and let us know your thoughts 👇
- Build a smart real estate agent (no license required). 🧠 LangGraph (by @langchain) + 📝 Tensorlake Contextual Signature Detection = ✅ Knows who signed ✅ When they signed ✅ If it’s ready to close Full tutorial + code linked below 👇
- Most "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
