Problem
Back-office teams manually re-typed data from invoices, forms, and emails into business systems — slow, error-prone, and expensive. The goal: AI pipelines that extract, validate, and route this data automatically with human review only on low-confidence cases.
Architecture
Document intake (email/upload) flows into an extraction service using vision-capable LLMs with strict Pydantic output schemas, a confidence-scoring layer that routes uncertain items to a human review queue, and n8n/custom workers that push validated data into downstream systems.
Challenges
- Confidence calibration: deciding when to trust extraction required per-field validation rules, not a single score.
- Auditability: every automated action stores its source snippet and model rationale for review.
Performance
Large reduction in manual processing time with human review needed on a small fraction of documents. (Draft metrics — replace with real numbers.)
Lessons Learned
The human-in-the-loop escape hatch is the feature that makes full automation trustworthy enough to adopt.
Future Improvements
Self-improving extraction from reviewer corrections and cross-document reconciliation.