What automation is good at
Reading fields consistently, re-checking arithmetic, spotting duplicates, matching against a supplier list, and never getting bored on invoice 240.
AI invoice processing, judged on your own invoices
Most small and mid-sized finance teams do not need a new accounting platform. They need the retyping to stop. Below is how automated invoice processing actually works, where it breaks, and how we prove it on a sample of your documents before anyone signs up to a rollout.
The manual loop we are replacing
An invoice lands in a shared mailbox. Someone opens it, reads the supplier and total, switches to the accounting package, types the header, types the lines, checks whether a purchase order exists, saves the PDF into a folder that follows nobody's naming convention, and forwards it for approval. Repeat a few hundred times a month. The cost is rarely one big number — it is attention taken away from work that needs a human.
What stays with people
Approving spend, resolving disputes, deciding how something is coded, and judging anything the system flags as uncertain.
How the workflow runs end to end
1. Capture
Invoices arrive in one place — a shared mailbox, a scanner drop folder, or a supplier portal export. Nothing changes for the people sending them.
2. Read and structure
The document is classified and its fields extracted: supplier identity, invoice and PO numbers, dates, currency, line items, tax lines, totals.
3. Validate
Arithmetic is re-checked, the supplier is matched to your master list, the PO or delivery note is matched where one exists, and duplicates are flagged.
4. Review
A person sees the invoice image beside the extracted values, with low-confidence fields highlighted. Approving is one action; correcting is inline.
5. Post and file
The approved entry is written to your accounting or ERP system and the source document is archived with a link back to the entry.
Nothing posts without the review step. That single design decision is what makes the rest safe to automate.
Is your invoice flow a good candidate?
Usually worth prototyping
- A steady flow of supplier invoices arriving as email attachments or scans
- Recurring suppliers with layouts that change occasionally
- Line-item detail that someone currently retypes into an ERP or accounting package
- A clear approval step that already exists in the business
Usually not, yet
- A handful of invoices a month — the review overhead outweighs the saving
- Documents that need a commercial decision before they can be coded at all
- No system of record to post into, only spreadsheets nobody trusts
How we prove it before you commit
We take one document type and a sample of invoices you have already processed by hand, build a working proof of concept, and compare its output to what your team actually entered. You get the numbers for your own documents, a running app to click through, and an honest read on the parts that are not ready. Client accounts open the full write-up, the screens and the live demo in the Coopsys Labs portal.
Start with a sample of invoicesQuestions we get asked
What is AI invoice processing?
It is software that reads an incoming invoice — PDF, scan or email attachment — extracts the fields you care about (supplier, invoice number, dates, line items, tax, totals), matches it against your purchase orders or expected suppliers, and hands a prepared entry to a person for approval before anything is posted to your accounting system.
How is it different from OCR?
Classic OCR turns pixels into text and relies on fixed templates for each supplier layout. A model-based approach reads the document more like a person does, so a new supplier or a rearranged layout does not require a new template. You still need validation rules, because a confident-looking wrong number is worse than a blank field.
Does it replace our bookkeeper?
No. In every build we deliver, a person approves what gets posted. The work that disappears is retyping and chasing — not judgement, supplier relationships or month-end review.
What accuracy should we expect?
It depends on your document mix, and that is exactly why we test on your own invoices rather than quote a number up front. The measurement that matters is how many invoices clear review untouched, and how quickly a reviewer can fix the rest.
Where does our data go?
We scope that with you before building. Options range from processing inside your existing cloud tenant to a self-contained deployment. Proofs of concept run on a redacted or limited sample unless you explicitly approve using live documents.
How long does a proof of concept take?
A narrow proof of concept is typically a matter of weeks, not quarters: one document type, one supplier group, one destination system, measured against invoices you already processed by hand.