Supply-chain document reconciliation
Purchase orders, goods receipts and invoices routinely disagree. Payment delays, supplier disputes and reconciliation work consume operations time every month.
AI matches documents across systems, flags discrepancies with evidence and routes exceptions to the right team with the full trail attached.
- 01
The system ingests POs, goods receipts, invoices and shipping documents from your ERP and portals.
- 02
It matches records across the three-way (PO/GRN/invoice) and four-way (plus shipping) reconciliation.
- 03
Perfect matches post automatically to your AP system.
- 04
Discrepancies are routed with the specific mismatch highlighted and the supplier history attached.
- 05
Exception-resolution analytics highlight suppliers and categories driving the most rework.
with 50 to 70 percent faster exception resolution.
Ranges drawn from production deployments and public enterprise benchmarks. For a specific rupee or dollar figure tailored to your volume, use the calculator below.
Prerequisites for a clean deployment.
- Digital PO, GRN and invoice feeds from your ERP and supplier portals
- Clean supplier master data
- Agreed tolerance bands for auto-matching
- An operations lead to own exception routing
Put your own numbers on it.
“At 1,000 documents a month and a loaded monthly cost of ₹1,50,000 per person, supply-chain document reconciliation would typically save ₹12 L to ₹14 L a year.”
Range uses this use case’s typical automation rate (80 to 95 percent) against the baseline time per task for documents work, with your cost per person converted at 160 working hours a month.
More in Operations
All use casesInvoice processing and AP automation
Accounts payable teams spend most of their month on the same repetitive work: pulling data from invoices, matching to purchase orders, routing for approval, and chasing exceptions.
Internal knowledge-base AI search
Organisational knowledge lives in five to ten different systems. Staff spend 30 to 60 minutes a day searching for information that already exists somewhere in the stack.
AI opportunity prioritisation
Organisations run too many AI pilots in parallel. Few reach production. There is no shared logic for deciding which bets to fund, which to park, and which to kill.