Invoice & Document Extraction

Photos and PDFs of invoices, POs and delivery notes become clean rows in your accounting system. Nobody retypes anything.

Discuss this See example work

Time to value: Pilot on your documents in ~2 weeks. Production in 4–6.
Deployment: Can run fully on your infrastructure
Category: AI & Intelligent Automation

A woman looking stressed while working at her laptop, surrounded by desk clutter

The manual version of this

Invictt AI layer

Ingest

Read

Extract

Check

Review and post

A woman relaxed at her desk, hands behind her head, smiling at her laptop

Once it's AI-assisted

01/The problem

Someone in finance opens a PDF, reads a number, types it into another window, and does that four hundred times a month. It is slow, it costs a salary, and it is where errors enter your books — a transposed total that nobody catches until reconciliation, three weeks later.

02/See it in action

What you'd actually see

A demonstration of the mechanism — not a live system reading your data.

Structured data

Vendor
Invoice #
Date
Amount

Validated→ ERP / accounting system

03/How it works

The operating model

What enters, what the system does with it, and what reaches you.

Input

Ingest

Documents arrive however they already do — a monitored inbox, a shared drive, a scanner, or a drag-and-drop page. Your suppliers change nothing about how they send things.

System

Read

Layout-aware OCR handles scans, photos and phone snapshots, including skewed, stapled and stamped pages. Text-native PDFs skip OCR entirely and parse exactly, which is both faster and more accurate.

System

Extract

Fields that matter come out structured — supplier, invoice number, dates, line items, tax, totals — and get normalised against your existing supplier list and GL codes rather than inventing new ones.

Check

Arithmetic is verified before anything is trusted: line items must sum to the subtotal, tax must reconcile, the total must match. Anything that fails a check, or lands below a confidence threshold, is flagged rather than guessed.

Human

Review and post

A reviewer sees only the flagged documents, with the source page beside the form and the relevant region highlighted. Approved records post into Xero, QuickBooks, Sage, your ERP, or a plain spreadsheet.

Read the detail behind each stage

Ingest

Documents arrive however they already do — a monitored inbox, a shared drive, a scanner, or a drag-and-drop page. Your suppliers change nothing about how they send things.

Read

Layout-aware OCR handles scans, photos and phone snapshots, including skewed, stapled and stamped pages. Text-native PDFs skip OCR entirely and parse exactly, which is both faster and more accurate.

Extract

Check

Review and post

04/What's included

What you actually receive

Concrete deliverables, not a statement of intent.

Ingestion from email, folder watch, or a hosted upload page

Layout-aware OCR tuned on your actual document mix, not a generic benchmark

Field extraction with a per-field confidence score

Arithmetic and business-rule validation before anything is written

Human-in-the-loop review queue with source-page highlighting

Write-back integration to your accounting system, ERP or spreadsheet

Duplicate detection, exception handling and retry logic

Accuracy dashboard and a scheduled drift review

Built with

PaddleOCR
Layout models
FastAPI
Postgres
n8n

Tooling is chosen per engagement. This is what this kind of build typically uses, not a fixed stack we sell.

05/Example work

All work

What this looks like in practice

Reference implementations, including what went wrong.

Reference Implementation2025

Removing manual invoice entry for a building materials distributor

Two full-time roles spent their mornings retyping supplier invoices into an ageing ERP. We replaced the typing with an extraction pipeline and a review queue — keeping the humans, moving them to the exceptions.

Read it

06/Also in this category