Document pipeline automation for a fintech operations team
An OCR → extract → reconcile pipeline with a full audit trail, replacing a four-person manual document operation.
A 4-person ops team, all day, every day
The company processed 12,000 vendor documents daily — invoices, contracts, statements. Every document was OCR’d, line-itemized, and reconciled by hand.
- Throughput was capped by headcount, and volume was growing 8% a month
- Manual keying produced reconciliation errors that surfaced weeks later
- Audit requests took days because provenance lived in people’s memories
The cost of leaving it alone
Document throughput gated revenue: vendors couldn’t be onboarded faster than paperwork could be processed, and error remediation consumed the team’s best people.
OCR → extract → reconcile, with an audit trail
Cloudflare Queues feed parallel workers. Tesseract handles clean documents; Claude takes the messy ones. Every extraction is logged and every reconciliation is human-reversible.
- Two-tier extraction: cheap OCR path with LLM fallback for degraded scans
- Confidence routing: anything under 0.85 lands in a human review queue
- KMS encryption at rest with a per-row audit log
- p95 processing latency of 1.2 seconds per document
Stack: Claude · Tesseract · Cloudflare Queues · S3 · Postgres
How it was built
- Week 1–2: corpus analysis across 40k historical documents; accuracy baseline defined with the ops lead
- Week 3–5: pipeline built end-to-end on one document type, running shadow-mode against the manual process
- Week 6–8: remaining document types added; reconciliation rules encoded with finance sign-off
- Week 9–10: cutover, monitoring, and training the two-person review team
What the numbers say
What happened next
Three of the four ops people moved into vendor relations and dispute work — higher leverage, less drudgery. The pipeline runs under a monitoring retainer; quarterly reviews add document types as the business expands.
This system is an example of Workflow Automation & Integrations work.
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