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How to Safely Parse and Redact Resume PDFs for B2B Hiring Workflows

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Developers building B2B hiring platforms can extract structured candidate data from PDFs by using a real PDF parser that captures positioned text, then passing normalized output to an AI model to propose typed fields. A deterministic validation step accepts or rejects each field before it reaches a template, ensuring only approved data is rendered in the final document. The source PDF should remain private, with logs recording only identifiers and counts rather than resume content. Template ownership is a critical decision: the team accountable for data disclosure should control the field allowlist, whether that is the application team, operations, or an external recipient. Recipient-owned templates must be treated as untrusted input, with unknown placeholders rejected and rendering done in an isolated worker.

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