Today we’re releasing OCRia-1, a frontier VLM for document parsing that sets a new SOTA in accuracy, cost, and latency. It not only sets a new high score on ParseBench (87.4), but also leads in three 3rd-party benchmarks: olmOCR-bench* (83.5 overall, 92.9 on tables!), https://t.co/ECb4n3o1ha
LiteParse v2.1 is here, and its bringing the fastest markdown output possible. In this release, we are fulfilling our top request: markdown output. But in the spirit of "lite"-ness, we are doing this completely LLM-free and fast. Not only is it fast, it also beats all other https://t.co/bdnSdNsMhA
We just built a Private Equity Assistant with LlamaAgents and the newly released LlamaCloud SDK. It can: 📊 Turn portfolio spreadsheets into structured, LLM-ready data with LlamaSheets 📂 Classify investor decks and extract key details with LlamaClassify and LlamaExtract 🤖 https://t.co/udycH9Fnng
Let's talk parsing tables. Two days ago we launched ParseBench,the first document OCR benchmark built for AI agents. This deep dive breaks down TableRecordMatch (GTRM), our metric for evaluating complex tables the way your pipeline actually consumes them: as records keyed by https://t.co/7ZQOUqo3hb
🚀 The team at @Google just released the Agents API, a service for building and running custom agents inside a sandboxed Linux environment, and we built a template that gives these agents access to LlamaParse / LiteParse, enabling them to process unstructured documents https://t.co/cS6Ydyt9Kt
