Banking AI Chatbot Team Lost Three Weeks to a Broken PDF Extraction Step

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A developer building a production banking AI chatbot discovered that standard PDF-to-text extraction was producing structurally corrupted output from loan policy documents. Tables were flattened into meaningless strings, headers merged with body text, and numbers from rate tables lost their column context entirely. Because loan documents rely heavily on structured tables for interest rates and eligibility criteria, garbled extraction meant the chatbot risked returning confidently wrong financial figures. Three different extraction approaches failed before the team adopted a document-intelligence pipeline that understood layout and could stitch multi-page tables back together. The episode highlighted how a single overlooked preprocessing step can undermine an otherwise well-engineered AI retrieval system.

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