AI Invoice Processing
PDFs, scans, photos and email bodies in — validated, matched records in your ERP out. Humans only touch the exceptions.
What it does
Supplier invoices arrive as PDFs, scans, photos and email text. The pipeline reads them, extracts every field, checks the math and the bank account, matches against purchase orders, and posts clean records to your accounting system. Your team reviews a short exception queue instead of keying in documents.
- Any layout, no templates. OCR + LLM extraction reads a new supplier's invoice on day one — no per-supplier setup, no broken templates after a redesign.
- Validation before posting. Line items must sum, subtotal plus tax must equal the total, the PO must exist, the IBAN must match the supplier record. Failures queue for a human with the extracted data pre-filled.
- Fraud hard-stop. Changed bank details block the invoice regardless of how confident the extraction is — the machine checks every invoice, every time.
- Three-way matching. Invoice, purchase order, goods receipt — matched automatically, posted to your ERP or accounting tool.
How it runs at Oido
This isn't a point tool — it's a pipeline on the same platform that runs your other agents and workflows: our OCR service handles scans and photos, LLM extraction runs on the model provider your data policy allows, n8n workflows handle the matching and posting, and approvals land in Slack or wherever your team already works.
Build it yourself on the platform, or have our team design, build and operate it with you — most finance teams pick the second. Either way you own the stack.
What to expect
High-nineties field accuracy on digital PDFs; lower on photos — which the pipeline flags rather than hides. The metric that matters is the zero-touch rate: the share of invoices posted with no human involvement. Good deployments get the majority of invoices there within the first months, improving as validation rules absorb your suppliers' quirks.
For the full technical walkthrough — pipeline stages, accuracy math, when it beats hiring another admin — read AI invoice processing explained.
Where it fits
Invoice intake usually follows order processing automation — same pattern, same infrastructure. Common homes: accounting firms, wholesale trade, corporate operations.
Want the numbers on your invoice volume? Free consultation — or see what AI automation costs overall.
FAQ
How does AI invoice processing work?
OCR converts the document to text, an LLM extracts the fields (supplier, invoice number, line items, tax, totals) regardless of layout, validation logic checks the math and bank details, and the invoice is matched to its purchase order and posted to your ERP. Anything that fails validation goes to a human queue with the data pre-filled.
How accurate is AI invoice OCR?
On clean digital PDFs, field-level accuracy in the high 90s is normal. Photographed or faxed invoices score lower — which is why the pipeline flags its own uncertainty instead of posting confidently wrong data. The number to track is zero-touch rate: the share of invoices posted with no human involvement.
Do I need templates for each supplier?
No. Template-based OCR broke whenever a supplier changed their layout. LLM extraction reads the invoice like a person would — a new supplier works on day one.
Which accounting systems and ERPs does it connect to?
Anything with an API — standard accounting tools via prebuilt integrations, industry ERPs and legacy systems via custom MCP connectors we build as part of the setup.
What does AI invoice processing cost?
Depends on volume and how messy the inputs are. Rough rule: if invoice handling consumes more than a few hours a week, automation pays for itself well within a year. We run the numbers on your actual invoices in a free consultation.
Does it help against invoice fraud?
Yes — the pipeline hard-stops any invoice whose bank details differ from the supplier record, every invoice, every time. That's a check tired humans skip and machines don't.