Tutorial2 min read
Invoice Extraction with AI: Accuracy That Holds
Extracting fields from invoices is easy to demo and hard to trust. The difference is everything around the model: schemas, validation and knowing when to ask a person.
TrueCodeAI Engineering
Agents, Voice & ML practice
- Published
Document AIAutomationTutorial
The pipeline
- Ingest from email, upload or scanner; detect document type.
- Extract into a strict schema: vendor, invoice number, dates, line items, tax, totals.
- Validate: line items sum to totals, tax rates are valid, vendor exists, invoice not duplicated, PO matches.
- Route: clean documents post automatically (or after light review); failures go to a person with the problem highlighted.
- Learn: every correction becomes an eval case.
Validation does the heavy lifting
Arithmetic and business rules catch most extraction errors without any AI: if the lines do not add up to the total, something was misread. A pipeline with strong validation can run a model that is not perfect and still deliver clean data.
Measuring accuracy
| Metric | Why |
|---|---|
| Field-level accuracy | Which fields fail and how often |
| Straight-through rate | Share of documents needing no human touch |
| Error escape rate | Wrong data that passed validation — the critical one |
| Review time per exception | Cost of the human step |
Frequently asked questions
Does it handle handwritten or poor scans?
Better than it used to, but quality drops. Those are routed to review more often.
Can it handle GST invoices?
Yes — GSTIN, HSN/SAC codes and tax splits are standard fields in our Indian builds.
What about other documents?
The same pipeline works for purchase orders, bank statements, delivery notes and forms.
