LlamaIndex

We're #hiring a new Member of Technical Staff, Infrastructure in San Francisco, California. Apply today or share this post with your network.

A blank cell can change the meaning of a forecast. Four times a year, the Fed's 18 top policymakers each put their forecasts on paper: where growth, jobs, inflation, and interest rates are headed. This is September 2026's edition, home of the "dot plot" that markets treat as the Fed tipping its hand. This release is the closest thing to the Fed saying what it plans to do. Most analysts will want to throw this documents to an AI agent, but this messy doc is dense: full of complex tables and charts that hold valuable context. All things that frequently trip up raw LLM APIs. The Fed’s September 2026 projections table includes a 2029 column, but its June comparison row leaves that cell empty. This is a common failure point for document parsers. We parsed page 2 with LlamaParse and checked the displayed GDP median excerpt against the original PDF. All nine numbers matched and the data stays aligned in the returned HTML. Try LlamaParse on a table where headers and missing cells matter. https://lnkd.in/eJ6zujZ5 Source: https://lnkd.in/gGMhTe93

We're #hiring a new Member of Technical Staff, Applied Research in San Francisco, California. Apply today or share this post with your network.

confidence scores only matter if they help you decide what to automate. for document extraction, that usually means knowing how much work you can safely accept at a given precision target. in our latest post, we look at confidence scoring through that lens, including: ✅️ confidence cutoffs ✅️ precision vs. recall ✅️ score coverage ✅️ score granularity ✅️ human review volume using ExtractBench, we compare how different extraction systems perform after confidence filtering. at a 97% precision target, LlamaParse Agentic Plus reached 66.48% recall on expected fields after filtering. the useful part of a confidence score isn’t the number itself. it’s whether you can use it to control automation and review in production. 👉️ read the full post: https://lnkd.in/gyjP8yAD

We are so excited to see LlamaIndex continue to grow across the org + world! 🥳 Welcome Chris Dickens, Liz VanZandt & Siddharth Niel P.! We are thrilled to have you all here. 🦙🚀

Excited for next Tuesday, Sept 29, and our monthly Daytona AI Builders Demo Night in SF! We’re hosting at WorkOS SF Office in partnership with LlamaIndex. Join us for an evening of great demos, conversations, and networking with the AI builder community. Event details & RSVP: https://lnkd.in/dFbFmpxj

We're #hiring a new Enterprise Account Executive - Digital Natives in San Francisco, California. Apply today or share this post with your network.

The fastest PDF-to-Markdown parser just got faster. ⚡️ With LiteParse v2.14.6, text-based PDFs parse about 25% faster. Across realistic documents, LiteParse processed pages at 2.8ms/page and 1.5× faster than the next-fastest local parser. LiteParse is open source and runs locally in Python, Node.js, Rust, or directly in the browser. Grab v2.14.6 → https://lnkd.in/e6b5Q-DZ Bench Docs → https://lnkd.in/gSCUCuCY

Grounded Confidence is here for Extract! 🦙 When your agents and workflows depend on extracted data, you need to know how accurate that data is. We’ve added confidence scores to give you a better read on extraction accuracy, field by field. Use them to decide which results your app can accept automatically and which need human review. Available on Cost Effective, Agentic, and Agentic Plus. Try it out on your docs -> https://lnkd.in/dw9tVawU