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.
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 s document engine no hacky pipelines required.
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.
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
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 🎉