Tensorlake - Parse documents like a human & build Python-based workflows
Tensorlake Cloud is a platform for document ingestion and data orchestration. Parse real-world documents with human-like layout understanding and build Python-based workflows at scale and ready for production.
Replies
Tensorlake
Hey Product Hunt 👋
We built Tensorlake Cloud because we kept seeing LLM apps and AI agents fail; not because of the models, but because of the data.
Enterprise documents are messy. A single page of a dense document might contain:
Metadata
Tables
Key-value fields
Visual indicators like strike-throughs or signatures
And that same information might be found in documents with slightly different layouts.
Not just another parser.
Tensorlake parses documents the way a human would: breaking them into semantic segments and applying specialized models per region, not just across the entire page. Then we let you build durable, Python-based workflows to automate processing on our managed GPU infrastructure.
A layout-aware document ingestion API that outperforms legacy tools on OCRBench v2 and RAGAS
A serverless orchestration engine that automatically scales and keeps pipelines fresh
It’s already running in production at hedge funds, utility companies, and fast-growing fintechs
We’re proud of the accuracy, developer experience, and the real impact it’s already having.
Read the announcement blog post
Join our community on Slack
Would love your feedback, questions, and support. Thanks for checking us out!
CoLaunchly
Huge congrats on the launch! 🔥 Love the focus on layout-aware parsing it’s such a real pain point in working with complex documents. Tensorlake looks powerful but refreshingly practical. Excited to see how this levels up data workflows for teams dealing with messy real-world inputs.
Tensorlake
Thanks@alex_cloudstar !
"powerful but refreshingly practical" is a great way to describe Tensorlake 💪
Congrats on Colaunchly 🔥
CoLaunchly
EverTutor AI
This is solving a real problem in a very smart way. Rooting for your success
Tensorlake
Thank you @suryansh_tiwari2 🎉
Tensorlake Cloud appears to offer a robust solution for the often-challenging task of document ingestion and data orchestration, with its human-like layout understanding and scalable Python workflow capabilities geared towards production environments.
Tensorlake
Thanks for your support@supa_l !
You really nailed what we’re aiming for because getting either one right is tough (document ingestion and workflows), but building both accurately and reliably is what turns a cool demo into something mission-critical teams can trust in production.
If you’ve run into specific pain points with document ingestion or orchestration, we’d love to hear about them 🔥 always looking to make Tensorlake even more useful for devs in the trenches.
Processing data is a very challenging task, and the emergence of Tensorlake has provided many new ideas, which is fantastic! I'm really looking forward to you bringing more changes.
Tensorlake
@hi_caicai Thank you so much! 🙌
We really appreciate the encouragement. Data processing is a hard problem, especially when documents are messy, inconsistent, or critical to workflows. Our goal with Tensorlake is to rethink this layer entirely so developers don’t have to fight the same battles over and over.
Can’t wait to share more soon, we’re just getting started!
Tensorlake
@hi_caicai Thank you! 🙌 We’re excited to be contributing new ideas to such a critical space. There’s a lot more coming soon—we’re just getting started. Appreciate your support and curiosity! 🚀
CastRecap
Tensorlake is a game-changer! The ability to parse documents with human-like understanding and build Python-based workflows at scale is impressive. It's exciting to see such innovation in document ingestion and data orchestration. Looking forward to exploring its capabilities further!
Tensorlake
Thanks@divyansh_tiwari7 ! Let us know if you have any questions or need any support. We're happy to help!
PageAI
Checked out your website & concluded this is a super cool & fresh app 🥬😃
I now have the urge of building 1000 CV parsing apps with a B2B focus.
Great sign when a product gets us excited about shipping apps!
Tensorlake
@dan_mindru love it!!
This is one reason I love working on dev tools, because I get equally excited about the types of new ideas/apps/experiences/impact good tools unlock for OTHER engineers 🔥
Impressive layout understanding for documents! 👍
Tensorlake
@shenjun Thank you! We have been focusing on some of the most complex and dense documents for critical workflows (e.g. ACORD liability insurance forms) so ensure we're able to extract data reliably and accurately with no hallucinations or data loss. Let us know if you have any questions or feedback!
Mukh.1
Big congrats on the launch! Love the focus on real-world document parsing and scalable Python workflows
We've just launched Mukh.1 too - a no-code platform that lets you build AI-powered workflows and agents with a simple drag-and-drop interface. Do check it out.
Tensorlake
@farrukh_anwaar Thanks so much! 🎉 We’ve worked hard to make Tensorlake powerful and practical for real-world use cases. Just checked out Mukh.1, very cool approach to making AI workflows more accessible. Congrats on your launch as well and best of luck! 🚀
Sounds like a powerful platform for scaling document automation, great to see Python-based workflows at the core!
Tensorlake
@neilkhanna99314 Yes! Let us know if you have any questions about it :)
Layout understanding + data orchestration? Someone finally cracked the code on intelligent document processing!
Pokecut
Tensorlake Cloud addresses a real pain point in enterprise AI: extracting structured, actionable data from complex, inconsistent documents.
Tensorlake
🚀 Thanks to everyone who’s checked out Tensorlake!
One of the biggest things we’re hearing from early users: it’s not just about parsing documents fast - it’s about preserving context and relationships across emails, PDFs, spreadsheets, and more.
We’d love to hear from you; what’s the hardest part of your AI pipeline when dealing with real-world data?
Tensorlake
🚀 New Feature: Signature Detection just launched in 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, forms, and parsing config
Whether you’re processing insurance packets, real estate contracts, or onboarding forms, you can now programmatically answer:
✅ Was this document signed?
✅ Who signed it?
✅ When?
✅ What should happen next?
No hacks. No manual checks. Just programmable document workflows.
🧪 Try it now in the Playground → https://tlake.link/playground
📖 Read the full blog post → https://tlake.link/signature-detection-with-tensorlake
📚 Docs → https://tlake.link/docs
Would love to hear how you’d use this — or what you’d want us to build next! 🙌