An async document-intelligence pipeline that reads, classifies, and extracts data from incoming legal mail for an EU261 flight-compensation firm, then writes structured results back to the CRM — with a safe manual-review fallback for anything uncertain.
One pass, zero bad writes
The full flow runs live end-to-end with a credential-isolated design and a proven safe fallback: well-formed documents are classified and extracted automatically, while garbage or uncertain inputs are flagged for human review instead of corrupting case data. Roughly half of incoming documents are born-digital and bypass OCR completely.
An EU flight-compensation firm (claims under EU Regulation 261/2004) received a constant stream of incoming PDFs — objection notices, court-cost invoices, handover letters, booking confirmations, customer claim forms. Each one had to be read, identified, and have its key fields entered into the CRM by hand. A previously built pipeline had stalled because its OCR engine never worked reliably, so nothing flowed end to end.
We were brought in as a task force to finish, fix, and cut the system over — not rebuild it. We fixed the OCR layer, then hardened a single async pass that runs OCR, a keyword pre-filter, classification, summarization, and field extraction, and emits a CRM-ready result. Born-digital PDFs skip GPU-OCR entirely by reading their text layer. The document that opens untrusted attachments holds no CRM credentials; results travel back over HMAC-signed callbacks to a workflow that is the only component allowed to write to the CRM. Anything low-confidence or unrecognized is routed to a human instead of written blindly. The system is built for safe, auditable automation — not blind throughput.
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