Skip to content

README.md

Latest commit

 

History

History
114 lines (83 loc) · 3.8 KB

README.md

File metadata and controls

114 lines (83 loc) · 3.8 KB
Raw

Module 02 — AI Builder: Form Processing

🎯 Learning Objectives

  • Build and train a custom document processing model
  • Understand structured vs unstructured documents
  • Extract invoice data and store in Dataverse
  • Implement confidence score checks

📖 Theory

Prebuilt vs Custom Document Processing

Prebuilt (Invoice) Custom Document Processing
Training needed No Yes (min 5 samples)
Works on Standard invoices Any structured document
Flexibility Fixed fields You define fields

Trainer insight: In 90% of enterprise projects, teams start with prebuilt, then discover their forms are non-standard and need custom models. Build custom from Day 1 if your documents vary.

Document Types

  • Structured — Fixed layout (tax forms, standard invoices)
  • Semi-structured — Variable layout, consistent fields (POs, receipts)
  • Unstructured — No fixed layout (contracts) → use Prompt/GPT instead

Field Types You Can Label

  • Text, Number, Date, Selection mark (checkboxes), Table (rows & columns), Signature

Training Best Practices

  • Minimum 5 samples; recommended 15-20 for good accuracy
  • Include variation — different vendors, fonts, page counts
  • Label every field in every sample
  • Include documents with some missing fields (real-world has gaps)

✅ Best Practices

  1. Use 15+ sample documents — 5 is minimum but gives poor accuracy
  2. Include document variations — different layouts, fonts, formats
  3. Log confidence scores — if confidence < 80%, route to manual review
  4. Version your models — keep old version until new one is validated in production
  5. Test with unseen documents — never test only with training samples
  6. Handle multi-page docs — test with 2-page and 3-page documents

🔬 Hands-On Lab — Invoice Extraction to Dataverse

Scenario

Accounts payable receives 50+ invoices daily from multiple vendors. Build a model to extract: Vendor, Invoice number, Date, Due date, Line items, Total — and store in Dataverse.

Step 1 — Create Custom Document Processing Model

  1. make.powerapps.com → AI Builder → Models → + New model
  2. Select: Document processing → Get started

Step 2 — Define Fields

Add these fields:

Field Type
VendorName Text
InvoiceNumber Text
InvoiceDate Date
DueDate Date
TotalAmount Number
LineItems Table (Description, Qty, UnitPrice, LineTotal)

Step 3 — Upload and Label 5+ Sample Invoices

For each document:

  1. Highlight text → select matching field
  2. For tables: use Table mode, draw box around entire table, map columns
  3. Label ALL fields on ALL documents

Step 4 — Train, Test, Publish

  1. Train → wait 15-30 min
  2. Quick test with unseen invoice → verify fields and confidence scores
  3. Confidence target: > 90% per field
  4. Publish when satisfied

Step 5 — Build Power Automate Flow

Trigger: File created in SharePoint /Invoices-Inbox

Step 1: Get file content

Step 2: AI Builder — Extract information from documents
  Model: your custom model

Step 3: Condition — confidence > 0.8?
  YES → Dataverse Add row (Invoices table)
  NO → Email AP team for manual review

Step 4: Apply to each (LineItems)
  Dataverse Add row (InvoiceLineItems table)

Step 6 — Test End to End

  1. Upload test invoice to SharePoint
  2. Check Dataverse Invoices table → verify record created
  3. Check InvoiceLineItems → verify each line item row

📋 POC Summary

Project: Invoice Automation System Business value: Eliminates manual data entry for 50+ invoices/day (~12.5 hours/week saved)

Next: Module 03 — Object Detection →