IDP: Document Automation That Scales

Overview

Intelligent Document Processing (IDP) turns heterogeneous documents into structured records. It blends OCR, layout understanding, and ML extraction with human-in-the-loop for quality. The objective is not 100% automation; it is predictable throughput and lower exception rates.

Patterns that work

Successful IDP programs standardize input capture, define confidence thresholds, and route low-confidence fields to QC. They design for drift—templates change, stamp placements move, and new document types arrive.

• Two-stage extraction: fast generic model → domain fine-tune for key fields.
• Confidence-driven QC with sampling and continuous learning.
• Traceability: preserve source snippets for every extracted field.

Use Cases

Common domains include invoices, claims, KYC, bills of lading, medical records, and lab reports. Value appears via cycle-time cuts, fewer downstream errors, and better analytics.

Rollout Plan

Start with a narrow but high-volume doc type. Define the golden truth process and acceptance thresholds, then integrate results into downstream systems with robust retries and idempotency.