#Reducto
Document parsing quality is often the hidden bottleneck in RAG and agent pipelines, and the claim that frontier models silently drop table rows is a production warning most teams have not internalised. Builders processing PDFs at scale can use the episode's guidance on model routing, build-vs-buy decisions, and continuous evals. The discussion of when 99% accuracy is still insufficient applies directly to contract review and extraction workloads.
The common thread is that data pipelines, not model size, decide whether document and video systems work — parsing, token selection and smart caching each cut cost by an order of magnitude. Builders working on OCR, video understanding or evaluation should copy the concrete tricks, like Reducto's parsed-page input and Meta's verdict caching. Merve Noyan's US$3-4 image-labeling pipeline is a ready template for cheap detection training.
