How much can good documentation save an AI agent in cost and time? Turns out, a lot.
We built a custom skill that teaches Claude how to parse PDFs more efficiently, then used real usage traces to find where it was wasting time and money (re-reading the same file over and over,
The world's best AI Document OCR
LlamaParse: cloud.llamaindex.ai
Docs: developers.llamaindex.ai/python/cloud/
Joined December 2022
- Contracts are where business commitments live, but most organizations still manage them manually, searching PDFs for renewal dates, chasing down payment terms, and hoping nothing slips through the cracks. The problem isn't just volume. Legacy OCR treats contracts like flat text,
- We're headed back to @databricks #DataAISummit to parse your PDFs next week 🦙 Catch our co-founder & CEO @jerryjliu0 twice: 📄 Automating Document Work with Long-Horizon AI Agents — databricks.com/dataaisummit/s… 🧱 The Agentic Stack: founder panel with LangChain, CrewAI, Agno +
- LlamaIndex 🦙 repostedAs frontier models (e.g. Fable 5) continue to push the task horizon of knowledge work automation, it becomes ever more important for humans to be able to audit decisions back to the source context. It is extremely easy for agents to cite an entire document or document page, but
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00:51Parsing a document accurately is one thing. Proving where every value came from is another. When a compliance team reviews an AI extraction, or an auditor needs to sign off on a figure pulled from a financial filing, "it came from this document" isn't enough. They need to see - LlamaIndex 🦙 repostedLiteParse, our open-source/Rust-based doc parser, runs so quickly that Claude Fable 5 doesn't think it's real 🔥 It is the fastest document parsing solution on the planet and a great choice for your AI document workloads. Check it out: github.com/run-llama/lite…LiteParse runs so fast that Claude Fable 5 doesn't think its real
- LlamaIndex 🦙 repostedClaude Fable 5 thinks document parsing is beneath it It is absolutely crushing on all reasoning-intensive/long horizon benchmarks: SWE-Bench Pro, FrontierCode, GDPval, Runescape, etc. But for document understanding tasks, it is roughly equivalent with Gemini 3 Flash inDay 0 Anthropic Fable 5 in ParseBench: We tested the model's advancements when it comes to document understanding. The model clearly peaks when it comes to adherence to the original text: 📃 Content faithfulness: 90.02% vs 86.19% (Gemini 3 Flash) and 86.81% (GPT-5.5) 🔢 Semantic
- Day 0 Anthropic Fable 5 in ParseBench: We tested the model's advancements when it comes to document understanding. The model clearly peaks when it comes to adherence to the original text: 📃 Content faithfulness: 90.02% vs 86.19% (Gemini 3 Flash) and 86.81% (GPT-5.5) 🔢 Semantic
- Parsing a document accurately is one thing. Proving where every value came from is another. When a compliance team reviews an AI extraction, or an auditor needs to sign off on a figure pulled from a financial filing, "it came from this document" isn't enough. They need to see
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