Finance AI AgentFinance AI Agent matches every supplier invoice to its order and delivery, checks spend against your policy and takes the routine work out of month-end close. Your finance team closes the books sooner with the same people, and no payment leaves without the approvals you already require.
80%
of invoices matched automatically
−3 days
to close the month
100%
of expenses checked against policy
Each invoice is compared with the purchase order and the delivery in your ERP. Differences, duplicates and missing orders are flagged with the documents attached, so early-payment discounts are taken again.
Prices above the order, short deliveries and invoices sent twice are caught before they reach a payment run, not found in an audit months later.
Bank feeds are reconciled every day, the close checklist is prepared and open items are chased, so your team spends the last days of the month on review, not matching.
Managers ask in plain words and get the figure with the entries behind it, so finance receives far fewer emails asking how much budget is left.
An accounts payable clerk asks why a supplier has not been paid. The agent checks the invoice against the order and the delivery, finds the difference and asks the supplier for a credit note. The payment is released only once it arrives.
Accounts payable clerkWhy hasn’t the Nordwerk invoice been paid?
Finance AgentInvoice NW-5521 (€14,200) is on hold: the purchase order covers 400 units but the delivery note shows 380. I’ve emailed Nordwerk for a credit note for the 20 missing units and will release the payment when it arrives.
Each one in short: the problem, the results, and how the agent handles the work.
Every invoice meant three screens: the invoice in the AP inbox, the order in the ERP and the delivery in the warehouse system.
Supplier, lines, VAT and bank details are read from emails, attachments and scanned post, in any of the group's three languages.
It finds the purchase order, confirms what the warehouse received and compares prices line by line, all inside the group's systems.
When something doesn't agree, the buyer who raised the order gets a message in Teams with the difference already worked out, and their answer is attached to the invoice.
Matched invoices are coded to the right account and cost centre and queued for Thursday's run, which the group controller approves.
Every morning someone in finance exported the night audit from each hotel, matched card and PayPal payouts by hand and pasted the results into a spreadsheet.
At 02:00 it matches every Stripe and PayPal payout to the bookings and night audit totals, and lists anything that doesn't agree.
At 06:30 each general manager gets yesterday's revenue, food cost and cash differences in Slack, with the two or three things worth a look.
Managers and head chefs ask in Slack and get an answer in seconds, from Xero and the budget, limited to their own hotel.
Supplier invoices are read and drafted in Xero, and expense claims are checked against the policy, for the finance team to approve.
Quantity surveyors and the accounts team compared each application with the contract, the previous payments and the latest site report by hand.
The claimed work, period and amount are read from each application, whatever template the subcontractor uses.
It checks the claim against the latest progress the site manager signed off, the contract value and everything already paid.
The proposed payment holds back uncertified work and the retention, with the working shown line by line.
The project manager approves in Teams, the subcontractor is told what waits and why, and the retention is logged with its release date.
Matching took most of a working day, so differences were found days later, when it was hard to ask the store what happened.
Till totals, card payouts, cash deposits and online payouts are gathered for every store as they arrive.
Each store's takings are matched to what reached the bank, allowing for card batches that close after midnight.
Timing differences are explained automatically.
Area managers get a daily takings report in Google Sheets, and finance gets the suggested entries for fees to approve.
The accounts payable team could only spot-check carrier invoices.
Each charge is read from PDFs and e-invoices: linehaul, fuel, waiting time, tolls and extras.
It compares every charge with the rate card for that lane and with what the shipment record shows actually happened.
If the record is unclear, the transport planner is asked in Teams whether the waiting time or extra stop really happened.
Correct invoices are approved for payment.
Field staff submitted expense claims in six currencies, and the small finance team coded each one to a grant by hand.
Expense claims, supplier bills and field transfers are matched to the programme, the grant and the budget line, with the reason recorded.
A cost that a grant doesn't allow is flagged before it is booked, with a suggestion of where it can go instead.
Budget holders are told in Slack when a grant is spending faster than planned, while there is still time to adjust.
Grant reports are drafted in Google Sheets with every number linked to its receipts, for the programme manager and finance to complete.
Illustrative scenarios with unnamed companies. The figures show the kind of improvement each setup is designed to deliver; actual results depend on volume, the systems connected and how the agent is configured.
We set up the Finance AI Agent around how your finance team works today, from the invoice inbox to the month-end close.
Week 1
We sit with the people who do the work, map the process step by step and find the systems that hold the information. You get a written plan before anything is built.
Weeks 2 to 3
We build and configure the agent around your process: your rules, your approvals, your wording, and connections to your databases and the tools your teams use.
Weeks 3 to 4
We install it inside your infrastructure, or in our cloud if you prefer, and test it with your team on real requests until the results meet the standard you set.
From week 5
We train your employees and administrators, hand over the controls and stay with you: monthly reviews, updates you approve, and the next process when you are ready.
Your team tests the agent on real invoices and bank lines within the first weeks. Most companies see the share of invoices it matches, and the days saved at month end, during the first close after going live.
It depends on your invoice volume, the systems it connects to and where it runs. After a first conversation you get a written proposal with the price, so you can compare it with what the work costs you today.
It takes over the typing, matching and chasing, so the same team can handle more invoices and spend its time on suppliers, budgets and analysis. Approvals, exceptions and decisions that need judgement stay with your people.
It works only from your own ledger, orders and documents, follows the tolerances you set and passes anything it is unsure about to a person. Every match and action is logged, so you and your auditors can review it.
No payment leaves without the approvals you already require. The agent prepares payment runs and releases only what your rules allow, and you can switch off any action at any time.
It can run on your own servers, with its AI models, so supplier prices, ledgers and bank data stay inside your company and no outside AI service sees them. We have no access to it, and your data is never used to train AI models.
No. It works with SAP, Oracle, Odoo, Xero and QuickBooks, plus your own ERP, and with Outlook, Gmail, Microsoft Teams, Slack and Expensify.
Yes. You decide which invoices it may approve alone and up to what amount, the price tolerance it works to, and which journals always need a person. Anything switched off becomes a review task for your team.
One person who knows how payables and the close work today, access to test versions of the systems involved, and a few hours a week from the team while we test.
Yes. Most companies start with supplier invoices or bank reconciliation for one entity, see the results, and then extend the agent to expenses and the close.
Tell us how many invoices your team handles, which systems hold your orders and ledger, and what slows the close down. You get a written proposal with the expected results, the setup, a timeline and a price.