Intelligent Document Processing

Executive summary

An automated pipeline that turns invoices, receipts, contracts, forms, and claims into structured data. It summarizes each document, extracts the key fields, stores it, routes it to the right place, and creates the follow-up tasks — replacing manual entry and inbox shuffling.

Business problem

Paperwork arrives as PDFs, scans, and email attachments that someone has to read, key in, file, and route by hand. It is slow, error-prone, and doesn't scale — and important details or deadlines get missed.

Architecture

AWS services

Data flow

1. 1

   A document is uploaded (browser presigned PUT) or arrives via an email/inbox integration into S3.

2. 2

   Lambda classifies the document type, then extracts fields with Textract and/or Bedrock (vision) and writes a summary.

3. 3

   The structured record is stored in DynamoDB and routed by type via EventBridge (e.g., invoices → Accounts Payable).

4. 4

   Follow-up tasks are created with due dates (e.g., 'Pay invoice #123 by …') and dispatched to the right team.

5. 5

   Every step is logged; downstream systems subscribe to the events.

Security considerations

• Documents encrypted at rest (S3 SSE) with short lifecycle expiry.
• Presigned uploads avoid exposing bucket credentials; least-privilege IAM.
• PII handling and retention configurable per document class.
• Full audit trail of extractions and routing decisions.

Cost considerations

• S3 + DynamoDB + EventBridge are pay-per-use.
• Textract and Bedrock are per-page / per-token — the main variable costs.
• Mock/keyword classification can run at ~$0 for demos; enable Textract/Bedrock for production accuracy.

Scalability

• Event-driven and serverless — scales with document volume automatically.
• S3-triggered async processing handles large batches.
• New document types add as classifiers + routing rules without re-architecting.

Deployment roadmap

Future enhancements

• Human-in-the-loop approval queue for low-confidence extractions.
• Email/scanner intake with automatic ingestion.
• Line-item reconciliation against POs and ledgers.
• Fraud/anomaly detection on amounts and vendors.