Launch HN: Pulse (YC S24) – Production-grade unstructured document extraction

Summary

Hi HN, we’re Sid and Ritvik, co-founders of Pulse. Pulse is a document extraction system to create LLM-ready text. We built Pulse as we realized that although modern vision language models are very good at producing plausible text, that makes them risky for OCR and data ingestion at scale.When we started working on document extraction, we assumed the same thing many teams do today: foundation models were improving quickly, multi modal systems appeared to read documents well, and for small or clean inputs that assumption often held. The limitations showed up once we began processing real documents in volume. Long PDFs, dense tables, mixed layouts, low-fidelity scans, and financial or operational data exposed errors that were subtle, hard to detect, and expensive to correct. Outputs often looked reasonable while containing small but meaningful mistakes, especially in tables and numeric fields.A lot of our work since then has been applied research. We run controlled evaluations on complex documents, fine tune vision models, and build labeled datasets where ground truth actually matters. There have been many nights where our team stayed up hand annotating pages, drawing bounding boxes around tables, labeling charts point by point, or debating whether a number was unreadable or simply poorly scanned. That process shaped our intuition far more than benchmarks alone.One thing became clear quickly. The core challenge was not extraction itself, but confidence. Vision language models embed document images into high-dimensional representations optimized for semantic understanding rather than precise transcription. That process is inherently lossy. When uncertainty appears, models tend to resolve it using learned priors instead of surfacing ambiguity. This behavior can be helpful in consumer settings. In production pipelines, it creates verification problems that do not scale well.Pulse grew out of trying to address this gap through system design rather than prompting alone. In...