Launch HN: Extend (YC W23) – Turn your messiest documents into data

Hey HN! We’re Kushal and Eli, co-founders of Extend (https://www.extend.ai/ https://www.extend.ai/). Extend is a toolkit for AI teams to ingest any kind of messy document (e.g. PDFs, images, excel files) and build incredible products. We built Extend to handle the hardest documents that break most pipelines. You can see some examples here in our demo (no signup required): https://dashboard.extend.ai/demo https://dashboard.extend.ai/demo I know you're probably thinking “not another document API startup”. Unfortunately, the problem just isn’t solved yet! I’ve personally spent months struggling to build reliable document pipelines at a previous job. The long tail of edge cases is endless — massive tables split across pages, 100pg+ files, messy handwriting, scribbled signatures, checkboxes represented in 10 different formats, multiple file types… the list just keeps going. After seeing countless other teams during our time in YC run into these same issues, we started building Extend. We initially launched with a set of APIs for engineers to parse, classify, split, and extract documents. That started to take off, and soon we were deployed in production at companies building everything from medical agents, to real-time bank account onboarding, to mortgage automation. Over time, we’ve worked closely with these teams and seen first-hand how large the gap is between raw OCR/model outputs —> a production-ready pipeline (LLMs and VLMs aren’t magic). Unlike other solutions in the space, we're specifically focused on three core areas: (1) the computer vision layer, (2) LLM context engineering, and (3) the surrounding product tooling. The combination of all three is what we think it takes to hit 99% accuracy and maintain it at scale. For instance, to parse messy handwriting, we built an agentic OCR correction layer which uses a VLM to review and make edits to low confidence OCR errors. To tackle multi-page tabular data, we built a semantic chunking engine which can detect the optimal boundaries within a document so models can excel with smaller context inputs. We also shipped a prompt optimization agent to automate the endless prompt engineering whack-a-mole teams spend time on. It’s built as a background agent to replicate the best prompter on your team, and runs in a loop with access to a set of tools (view files, run evals, analyze results, and update schemas). The most surprising part of this whole experience has been seeing how many crazy PDF formats are out there! We've run into everything from supermarket inventory magazines, pesticide labels, construction blueprints, and satellite manufacturing plans. Everything described above is live today. You can see it in action here (no signup): https://dashboard.extend.ai/demo https://dashboard.extend.ai/demo. To upload your own files, you can log in and do so (we’re adding free usage credits to all accounts that sign up today). We’re excited to be sharing with HN! We’d love to hear about your experiences building document pipelines. Please try it out, and share any and all feedback with us (e.g. hard documents that didn’t work, feature requests).