Document Extraction AI Workflow. @ai-document-extraction

The Document Extraction AI Workflow turns a document stream — invoices, contracts, claims, onboarding packs, delivery notes — into validated structured data flowing into your systems. Modern LLM-based extraction handles the layouts and phrasing variation that broke template-based OCR, and this sprint wraps it in the engineering that makes it production-grade: classification, field-level validation, confidence scoring, and an exception queue for the documents that genuinely need eyes. The finish state is measured on your own documents: agreed extraction accuracy on a held-out test set, with straight-through processing for confident cases and humans reviewing only exceptions.

Document intelligence is core Scrums.com GenAI territory. The honest design rule: the system must know what it does not know — a wrong value extracted confidently is the failure mode everything here is built to prevent.

Document Taxonomy & Field Schema

Your document types classified and each given a field schema — what to extract, in what format, against which validation rules — agreed with the teams who consume the data.

Extraction Pipeline

Ingestion from email, upload, SFTP, or scan sources; classification; and LLM-based field extraction that handles layout variation, multi-page documents, and tables.

Validation & Confidence Scoring

Field-level validation against formats, reference data, and cross-field rules (totals that must sum, dates that must order), with per-field confidence deciding what passes straight through.

Exception Queue & Systems Delivery

A review UI where low-confidence or failed-validation documents are corrected — corrections feeding accuracy improvement — and clean delivery of validated data into your ERP, database, or workflow.

1. Scope — Fix document types, field schemas, validation rules, and the accuracy bar on a sample of real documents.
2. Build — Build the pipeline and validation; measure against a held-out test set until the bar is cleared.
3. Handover — Production go-live, the exception queue in your team's hands, and accuracy monitoring that catches drift.

The Document Extraction AI Workflow is a menu item on the Scrums.com delivery catalog, available at every plan tier. Add it to your plan backlog and your delivery team schedules it like any other item — scoped, tracked, and reported through the SEOP. See Delivery Plan Tiers.

What accuracy should we expect?

It varies by document quality and field type, which is why the sprint fixes the bar on your sample set at scoping and proves it on held-out documents — a number you verify, not a brochure claim.

Can it handle handwriting and poor scans?

Often, with lower confidence — which is exactly what the confidence scoring is for: hard documents route to the exception queue instead of producing silent errors. The sample review at scoping sets expectations per source.

Where does the extracted data go?

Wherever your process needs it — ERP, accounting platform, database, or workflow tool. If the destination integration itself is substantial, items like the ERP Integration Package pair with this one.