Team
AI automation for finance and accounting that captures invoices, prepares reconciliations and drafts reports, with validation checks and human approval before anything touches the ledger.
Sound familiar?
Finance teams spend a surprising share of their time on data entry and preparation: typing invoice lines, matching bank transactions, collecting receipts and rebuilding the same reports every month. It's work that has to be right, which is exactly why people are nervous about automating it.
We share that caution. Our approach to AI automation for finance and accounting puts accuracy and approvals first. The AI reads and prepares; deterministic checks verify; a person approves. Totals must add up, tax amounts must match the rate, suppliers must exist, duplicates are flagged, and nothing is posted to the ledger without the approval rules you set.
The most common starting point is document processing for accounts payable: supplier invoices are read from an inbox, matched to purchase orders and suppliers in Xero, QuickBooks or NetSuite, and queued as drafts for approval. From there, teams often add reporting automation that pulls actuals, budgets and KPIs into a monthly pack with first-draft variance commentary for the finance lead to edit.
We're software engineers, not accountants, and we don't give accounting or tax advice. We work alongside your finance team or external accountant so the automations follow your chart of accounts, controls and policies. If you run a firm that does this work for clients, see how it applies to professional services.
The problem
Most finance teams aren't short of skill. They're short of time, because a lot of the month goes on preparing data before anyone can analyze it.
Supplier invoices arrive as PDFs, scans and email bodies. Each one is opened, coded, matched to a purchase order if there is one, and keyed into the accounting system. A wholesale business might handle a few hundred a month, each taking a few minutes, with errors that only surface at payment run.
Bank feeds help, but plenty of transactions still need a person to work out what they are: a payout that bundles fifty Shopify orders, a customer paying three invoices in one transfer, a card charge with a cryptic description.
Chasing overdue invoices is uncomfortable and easy to postpone. Reminders go out irregularly and the tone depends on who's sending them.
Receipts get forwarded, photographed or lost. Someone matches them to card transactions at month-end and chases the missing ones.
The management pack is exported, pasted into Excel, formatted into slides and annotated by hand, every month, in the same layout.
All of this is preparation, and preparation is where automation helps most. Judgment on coding unusual items, approving payments and signing off the close stays with your team. What changes is that they start from a prepared, checked draft instead of a blank screen.
A language model is good at reading a messy invoice and finding the supplier, date, lines and totals. It is not a calculator and it shouldn't be trusted as one. So every number it extracts is verified in ordinary code: lines are re-added, tax is recalculated, the supplier's bank details are compared with what's on file, and the invoice number is checked against previous bills. Changed bank details are always flagged for a person, because that's a common pattern in invoice fraud. The model reads; the rules check; your team approves.
Where to start
Invoices from an AP inbox are read, matched to the supplier and any purchase order, coded using your history and rules, and created as draft bills in Xero, QuickBooks or NetSuite. Checks on totals, tax, bank details and duplicates run first, and approvers sign off in Slack, Teams or the accounting system.
Unmatched bank lines are compared with open invoices, payouts and remittance emails, and the automation proposes matches with its reasoning. Payment platform payouts from Stripe or Shopify are broken down into their underlying orders and fees. A person accepts or rejects each suggestion.
Reminder drafts are prepared on a schedule you set, adjusted for customer and how late the invoice is, with a copy of the invoice attached. Key accounts can be held back for a personal call, and nothing goes out without the rules you approve.
Staff forward or photograph receipts, the automation reads them and matches them to card transactions, and anyone with missing receipts gets a short weekly nudge instead of a month-end scramble.
Actuals, budget and KPIs are pulled from your accounting system and spreadsheets into a consistent report. The numbers come from the source systems, never from the model; the AI drafts variance commentary for the finance lead to correct and approve.
Messages to accounts@ or finance@ are sorted into invoices, statements, remittances, supplier queries and spam, with each routed to the right workflow or person. Supplier questions about payment status get a draft reply built from the actual bill status in your accounting system.
Integrations
Xero, QuickBooks Online, NetSuite and Sage Intacct through their official APIs, writing drafts that follow your chart of accounts and tracking categories.
Bill.com, Ramp, Dext and similar tools where you already use them, or a lightweight custom approval flow where you don't.
Stripe, PayPal, Shopify Payments and bank feeds for payouts, fees and transaction data used in reconciliation.
Google Sheets, Excel, Looker Studio and Power BI for budgets, models and the reports your leadership already reads.
Slack and Microsoft Teams for approval requests with the document and checks attached, so approvers can decide without logging in elsewhere.
FAQ
No, unless you explicitly choose that for a narrow, low-risk case. By default the automation creates drafts and a person approves them under your existing approval rules. Payments are never released automatically.
Typed PDFs are read very reliably, scans and handwriting less so. Accuracy doesn't depend on the model alone: we check that lines sum to the total, tax matches the rate, the supplier and bank details match your records and the invoice isn't a duplicate. Anything that fails a check goes to a person with the problem highlighted.
Yes. Every automated step records the source document, what was extracted, which checks ran, what was suggested and who approved it. That log sits alongside the normal history in your accounting system, which helps at year-end and during audits.
We send only the data a step needs to the model, using providers' business terms that exclude training, and keep credentials in a secrets manager with the narrowest permissions the accounting API allows. Automations can run in your own cloud account if your policies require it.
No. It removes data entry and preparation so they can spend time on review, judgment and advice. We don't provide accounting or tax advice ourselves, and we design the controls with your finance team or accountant.
Tell us where the hours go in your finance process. We'll suggest a first AI automation for finance and accounting with the checks and approvals built in from day one.
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