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  "text": "Many AI agents in finance rely on extremely high quality context engineering from documents 📑\n\nThey can be roughly divided into two categories:\n1️⃣ Repetitive, operational work common in back-office use cases - invoice processing, loan origination, KYC\n2️⃣ Assistive agents for open-ended research and generation of reports/presentations - e.g. diligence, equity research\n\nWe gave a workshop last week in NYC on how to build a high-quality document context layer to enable these AI agent use cases. At this stage, you need a rigorous OCR layer, evaluation checks, and good UI/UX for HITL review/audit - even a slight mistake in number can have catastrophic consequences downstream.\n\nCheck out the resources below:\n✅ My slides: talk a lot about document processing and the general landscape of knowledge work: https://t.co/qpxRK0yzdc\n✅ Logan’s repo on building an agentic document parsing pipeline over financial documents, with full HITL review: https://t.co/VlrRzV5Vi6\n\nOur core mission is extracting the highest-quality document context for AI agents in finance and more. Come talk to us if you’re facing relevant challenges: https://t.co/Ht5jwxSrQB",
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      "description": "Parsing the world's hardest PDFs @llama_index. cofounder/CEO\n\nCareers: https://t.co/EUnMNmbCtx\nEnterprise: https://t.co/Ht5jwxSrQB",
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