Qualified people approve. The system prepares and checks; a qualified person approves anything with tax or statutory consequence. We implement document AI and reasoning to automate invoicing, expense reports and monthly ledger reconciliation.
The system prepares and checks; a qualified person approves anything with tax or statutory consequence. We implement document AI and reasoning to automate invoicing, expense reports and monthly ledger reconciliation.
Finance departments are drowning in manual processes that should have been automated a decade ago. Invoices arrive as PDF attachments, scanned images, and forwarded emails in a dozen different formats. Staff manually key line items into accounting systems, cross-reference bank statements against spreadsheets, and chase down receipt approvals through email threads. This isn't financial management — it's expensive data entry. AI invoice processing replaces that workflow with a pipeline that prepares, checks and routes — and hands anything doubtful to a person.
Our financial reconciliation automation begins at the point of document ingestion. Advanced OCR data extraction AI, powered by multimodal vision models, processes incoming invoices regardless of format — structured PDFs, photographed receipts, handwritten notes, or embedded email tables. Unlike legacy OCR that struggles with layout variations, our AI understands document semantics. It identifies vendor names, line item descriptions, unit prices, quantities, tax rates, and payment terms through contextual reasoning, not rigid template matching. Field-level accuracy is measured on your own document sample during the pilot rather than quoted from a benchmark; what the design guarantees is that ambiguous fields are flagged for one-click human verification instead of becoming silent errors.
Once extracted, the data flows into an intelligent categorization and reconciliation engine. Expense categorization AI assigns each transaction to the correct general ledger account using historical patterns and natural language understanding of line item descriptions. A charge from "AWS" is categorized as cloud infrastructure, not office supplies — and the system learns your specific chart of accounts, not a generic template. For accounts payable automation, the system matches purchase orders to invoices to receiving reports in a three-way match, flagging discrepancies in quantity, pricing, or terms before any payment is issued. Monthly bank reconciliation, traditionally a multi-day ordeal, runs autonomously: the AI cross-references every bank feed transaction against your receivables, payables, and journal entries, resolving matches and surfacing anomalies for review.
Controls are woven into every layer, and it is worth being precise about what that means. VAT fields are checked against the rates you configure per jurisdiction. Audit trails are generated automatically for every action the system takes. Rule engines flag entries that breach the policies your finance team configures, before they are posted. What a rule engine cannot do is make you compliant with SOX, IFRS or local GAAP — that is a management responsibility, and under SOX §404 it is explicitly one that requires human oversight rather than the absence of it. The system is designed to reduce error rates and to support your control framework; sign-off and statutory compliance remain with your accountants and auditors. We do not provide accounting or tax advice.
Multimodal vision models process every incoming document — PDFs, scans, photos — extracting structured financial data with semantic understanding.
Transactions map to your chart of accounts, three-way PO matching flags discrepancies, and bank feeds reconcile against your ledgers autonomously.
Configurable rule engines check entries against your policies and VAT rates while audit trails generate automatically — designed to shorten the close, with your accountants signing it off.
Our multimodal vision models analyse incoming documents — PDFs, scanned images, photos, and email attachments — using contextual semantic understanding rather than rigid templates. The AI identifies vendor details, line items, quantities, unit prices, tax rates and payment terms through contextual reasoning, then stages the structured data for your accounting system. Accuracy is measured on your own documents during the pilot, and anything the model is unsure of goes to a person.
We integrate natively with QuickBooks, Xero, NetSuite, Sage, FreshBooks, and SAP. For enterprise ERPs or custom accounting platforms, we build bespoke API connectors or deploy browser automation agents to ensure seamless data flow regardless of your financial infrastructure.
We measure it on your own document sample during the pilot and report the field-level figure, rather than quoting a number from someone else's corpus — accuracy on clean supplier PDFs and on photographed receipts are very different problems. Unlike template OCR, the system reads document structure semantically, so it handles invoices it has not seen before. Ambiguous extractions are flagged for one-click human verification, never silently accepted.
No system can, and we will not claim otherwise. Rule engines flag entries that breach the policies your finance team configures, VAT fields are checked against the rates you set per jurisdiction, and complete audit trails are generated for every automated action — all of which supports your control framework. But SOX (US), IFRS, local GAAP and, in Spain, the Plan General Contable are obligations your management and auditors carry. Under SOX §404 in particular, management control is the point; a system that removed human oversight would be the problem, not the solution. Sign-off stays with your accountants. We do not provide accounting or tax advice.
The system pulls your bank feed transactions and cross-references each one against receivables, payables, and journal entries using amount, date, counterparty, and reference matching. Confident matches are reconciled automatically; ambiguous or anomalous transactions are surfaced for human review with suggested matches. The design is aimed at shortening the close rather than eliminating it — the matching runs unattended; the sign-off does not.
Yes. Human oversight is built into the workflow. Employees drop receipts into Slack, the AI categorizes each expense and validates it against company policy, and the result is staged for one-click manager approval. Nothing posts to the ledger without passing your configured approval rules, and every decision is logged.
Give your finance team the preparation and checking, and let them spend their judgement on the exceptions.
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