Invoice processing softwarethat closes the loop.
Invoices, receipts, purchase orders and statements become validated, structured data: extracted against your schemas, checked until the numbers add up, and routed into your finance systems with review only where it is needed.
Automate accounts payable end to end: invoice data extraction, validation of totals and IDs, cross-checks against related documents and delivery to your finance systems.
Manual entry is only the visible cost.
Invoices and receipts still get typed into finance systems by hand, and every typo becomes a downstream correction: mismatched totals, wrong supplier IDs, reconciliation overhead at close.
Basic invoice OCR does not fix this, because reading characters is not the problem. The problem is getting validated data: totals that reconcile, tax rates that compute, line items that match the purchase order. That takes extraction plus validation plus a human when something is off.
Accounts payable automation, step by step.
One pipeline from inbox to system of record.
1. Ingest
Invoices arrive by email, S3, SFTP or API. No pre-sorting: mixed inboxes are fine.
2. Classify
Each document is classified against your types: invoice, receipt, purchase order, statement.
3. Extract
Schema-driven invoice data extraction: header fields, line items and complex tables, from any layout without templates.
4. Validate
Totals are recalculated, tax rates checked, supplier IDs verified; related documents are cross-checked. Failures park for human review.
5. Deliver
Clean records flow to your ERP or accounting system via webhook, database or S3.
What changes in finance ops.
Less manual entry
Typing gives way to reviewing exceptions: the queue holds only documents that failed a check.
Fewer corrections
Validation catches mismatches before they reach the ledger, not after the close.
Faster close
Reconciliation starts from validated data, so cycle ends stop depending on backlog typing.
Numbers to hold the system to.
Measure the workflow, not the demo.
Manual minutes per document
Time from arrival to system of record that a human actually spends.
Mismatch rate
Share of documents failing validation or cross-checks; it should fall as suppliers stabilize.
Cycle time
Intake to posted record, measured across the whole inflow rather than the easy cases.
Invoice processing questions
What finance teams evaluating AP automation ask most.
OCR reads characters; it does not tell you whether the invoice is right. MiruIQ extracts against a schema and then validates: totals are recalculated, tax rates checked, IDs matched, and related documents cross-checked. Anything that fails parks for human review instead of flowing into your ledger. The deliverable is validated data, not text.
Yes. Cross-document validation compares fields across a bundle: invoice line items against the purchase order, amounts against the statement, supplier data against your known-good mappings. Mismatches surface with field-level detail so a reviewer resolves them in one look.
Plans are public and sized by document volume, so you can compute your per-document rate before any sales conversation. Simple and complex pipelines cost the same, and the 14-day free trial covers 200 documents to validate accuracy on your own invoices first.
See it on your own documents.
The 14-day free trial covers 200 documents: enough to define your schemas, run real files and measure the results before anyone calls you.