Document intelligence · Invoice processing automation
Every supplier invoice has the same fields. Your AP team shouldn't have to find them by hand each time.
Banao builds invoice-processing automation that reads supplier invoices in any format — scanned, photographed, PDF, or EDI — extracts the header, line-item, and tax fields, runs a three-way match against your purchase orders and goods receipts, and posts the cleared result into your ERP without a person retyping anything.
We deliver the full pipeline as one system: classification, extraction, validation, exception routing, and the straight-through posting that removes AP keying on the invoices that clear. The invoices that don't clear — a short shipment, a pricing discrepancy, a new vendor — route to a reviewer with the evidence attached, not as an inbox full of PDFs to decipher.
Shared-services finance team (anonymized)— Supplier invoices extracted, matched against POs, and posted to the ERP straight through.
What an invoice-processing pipeline covers
Automating invoice processing is not one model call. It is classification, extraction, three-way matching, exception routing, and ERP integration — we build all of them as a single deliverable.
Invoice classification and splitting
Sorting a mixed batch into invoices, credit notes, remittance advices, and statements — and splitting a multi-document PDF before a single field is extracted, so the right rules apply to the right document.
Header and line-item extraction
Pulling invoice number, date, supplier details, and PO reference from the header, then extracting every line-item description, quantity, unit price, and amount from tables in any layout — not just the vendor templates mapped last year.
Tax, discount, and totals reconciliation
Extracting GST, VAT, and withholding amounts, verifying that line totals add to the invoice total, and flagging arithmetic mismatches that arrive more often than most AP teams admit.
Three-way match against PO and GRN
Checking each extracted invoice line against the purchase order and goods-receipt note — quantity, unit price, and amount — so a short delivery or a price deviation surfaces before the invoice posts, not after.
New-vendor and new-layout handling
Reading invoices from vendors never seen before, without a template for their layout, so a first invoice from a new supplier doesn't block the queue or demand a manual setup before processing can start.
Confidence scoring and exception routing
Every extracted field carries a confidence score. Invoices that clear all validation rules post straight through; those with a discrepancy, a low-confidence read, or a missing PO reference route to the AP reviewer with the evidence — not just the flag.
AP reviewer queue
An interface that shows the invoice image, the extracted fields, and the validation failure side by side — so a discrepancy is resolved in seconds instead of opening the PDF, finding the ERP entry, and piecing together what went wrong.
ERP and payment-system integration
Posting the validated invoice into SAP, Oracle, Microsoft Dynamics, Tally, or your own ERP via its API, so an approved invoice advances to payment without a person touching it again.
Why supplier invoice variability is harder to automate than it looks
The AP team's problem is not that invoices contain too many fields. It is that the same fields arrive in a different position on every supplier's stationery, renamed in a different way, split across a table in one layout and buried in a header in another. A rule-based parser built for one vendor breaks silently when that vendor redesigns their PDF. A parser built for fifty vendors becomes a maintenance liability the moment the fifty-first arrives.
We address variability by building layout-agnostic extraction — a model that understands the semantic role of a field, not its pixel position on the page. The same model reads a clean machine-generated invoice from a large supplier and a low-resolution scan from a small one. New vendors add no setup cost. The edge cases — multi-currency, foreign tax regimes, bundled services without line items — are designed into the pipeline, not discovered in production.
No per-vendor template maintenance
We build extraction that works on any layout, so the cost of adding a new supplier is close to zero — not a ticket to your AP systems team and a two-week wait for a new template.
Edge cases are part of the spec
Multi-currency invoices, credit notes in unusual formats, and invoices with no PO reference are handled by design — with explicit rules for each case, not a silent failure and a document stuck in limbo.
Accuracy measured on your invoices
We build a labelled ground-truth set from a sample of your real intake and measure field-level accuracy and straight-through rate against that — not against a published benchmark on someone else's data.
Three-way match is where invoice automation earns its cost
Extracting the invoice number and the total is the easy part; every OCR product does that. The part that decides whether the system is worth running is the three-way match: comparing each invoice line against the purchase order and the goods-receipt note for quantity, unit price, and amount. A discrepancy there is a real AP problem — a short delivery, a price change nobody approved, a duplicate invoice — and catching it before the payment run is the financial control that justifies the automation.
We wire the three-way match into the ERP or procurement system where your POs and GRNs already live, so the check runs against the actual record. Invoices that match clean go straight through. Those that don't route to the AP reviewer with the discrepancy highlighted, the PO and GRN details attached, and the resolution options in front of them — not a query email to procurement and a two-day wait.
Match at the line level, not the total
A total that matches can hide a line-item discrepancy. We match every extracted line against the PO line and the GRN confirmation, so a partial delivery or a substituted item is caught even when the arithmetic works out.
Tolerance rules built to your policy
Some tolerance on price and quantity is expected and acceptable. We encode your AP policy — a 2% price tolerance, a rounding allowance — so the system flags genuine discrepancies without raising an exception on every rounding difference.
Duplicate invoice detection
Duplicate invoices — same supplier, same amount, slightly different date or reference — cost AP teams real money. We check each incoming invoice against your posting history before it enters the matching step.
We run AI on our own financial operations
Banao is a ~300-person engineering company that runs the AI systems it builds on its own operation. Vikaas runs our demand generation end to end; InterviewGod screens our own hires before a recruiter opens the applicant pile. We depend on AI that has to be right on real, messy inputs — or we feel the cost ourselves.
The standard we hold our own AI to is the standard we bring to yours. When we scope an invoice-processing pipeline, we are measuring what 'production ready' means by the bar we would accept on our own accounts payable — not by a demo accuracy figure on someone else's clean data.
- VikaasRuns Banao's own demand-generation pipeline end to end, in production daily.
- InterviewGodScreens Banao's own inbound job applications before any recruiter opens the pile.
When invoice-processing automation is the wrong starting point
Automating invoice processing is not always the right first step, even in an AP function that clearly needs it. We will say so on the first call rather than let you commission a build that solves the wrong problem:
- You already receive structured data: if your largest suppliers send invoices via EDI or a supplier portal that exports structured data, start there — OCR and extraction on a PDF of data you can get cleanly as a feed adds cost with no gain.
- You have no purchase-order discipline: if a large share of invoices arrive without a PO reference because the business approves spend after the fact, the pipeline will route most invoices to exceptions. Fix the PO process first, then automate the matching.
- Your ERP is the bottleneck: if invoices are held up by an approval workflow inside the ERP rather than by manual data entry, adding a front-end extraction layer speeds up keying but not the payment cycle time.
- One layout from one supplier: if the invoice type you want to automate comes entirely from a single supplier in a consistent format, a simpler parser or an EDI connection is cheaper and more reliable than a layout-agnostic model.
- Volume too low to build an evaluation set: if the invoice type arrives a handful of times a week, there is not enough data to build a labelled ground truth, and without a ground truth you cannot measure — or be confident in — the model's accuracy.
How we start — measure your straight-through rate before we build
The number that decides whether an invoice-processing pipeline pays for itself is the straight-through rate on your actual intake, not on a curated sample. We measure that first.
- AI Discovery Sprint2 weeks · fixed price
We take a real sample of your invoice intake, run extraction and three-way match against your PO and GRN data, and hand back a pipeline design with the achievable straight-through rate, exception-handling plan, and ROI maths. If you proceed to the build, the Sprint cost is credited against it.
- Build and integrate
We build extraction, validation rules, three-way match against your ERP data, the exception queue, and the posting integration — as a single deliverable, not a model handed off to your IT team to wire up.
- Production and improvement
We deploy with monitoring on straight-through rate and field-level accuracy, a human-review loop for exceptions, and a path to improve the pipeline as new vendors, layouts, and invoice types arrive.
Frequently asked questions
What is invoice processing automation?
It is using AI to extract the fields from a supplier invoice — header, line items, tax, totals — validate them against your purchase orders and goods receipts, and post the result into your ERP without a person retyping the data. The goal is a high straight-through rate on invoices that match cleanly, with exception routing for those that don't.
Can it handle invoices from suppliers we have never seen before?
Yes — that is the point of layout-agnostic extraction. We build a model that reads the semantic meaning of a field rather than its position on a specific template, so a first invoice from a new vendor processes without anyone setting up a template or mapping fields by hand.
How does three-way matching work in the automation?
We connect the pipeline to the ERP or procurement system where your purchase orders and goods-receipt notes already live. After extraction, each invoice line is matched against the PO line for quantity and unit price, and against the GRN confirmation for delivery. Invoices that pass route to straight-through posting; those with a discrepancy route to the AP reviewer with the detail attached.
What happens to invoices the system cannot process cleanly?
They route to a reviewer queue that shows the invoice, the extracted fields, and the reason for the exception side by side — a pricing discrepancy, a missing PO reference, a low-confidence read on a damaged scan. A reviewer resolves it in seconds rather than hunting across systems to piece together what went wrong.
Which ERP systems can it post into?
SAP, Oracle, Microsoft Dynamics, Tally, and custom or in-house ERP platforms via their APIs. If your ERP has an API, we integrate with it. For older systems without a modern API, we have built retrofit integrations — integration is part of the build deliverable, not a separate project.
How accurate will the extraction be on our invoices?
Accuracy depends on the invoice quality and field complexity, so we measure it on your own documents in the Discovery Sprint before quoting a build. The two numbers that matter are field-level accuracy and the straight-through rate. We tune the confidence threshold so the system is honest about uncertainty rather than posting a confidently wrong value.
Can it process invoices in multiple languages and currencies?
Yes. We build extraction for the languages and currency regimes your supplier base actually sends — Arabic, Hindi, or other languages alongside English, multi-currency with correct tax line handling. This is designed into the pipeline from the start, not added as an afterthought.
How long does it take to build and go live?
A common path is a 2-week Discovery Sprint to validate the achievable straight-through rate, then a 6–10 week build and integration, then a monitored ramp that starts with human approval on every posting and widens as the numbers earn it. Banao's engineering bench means the build starts in weeks, not months.
Bring your hardest invoice layout and your current straight-through rate
In 45 minutes we will tell you what a production-grade invoice-processing pipeline would achieve on your real intake — and what building it would take.
Book a 45-min scoping call