Bookkeeping team spending half their week downloading bank statements and manually categorising transactions that follow the same patterns month after month?
Expense reports submitted as photographed receipts in a WhatsApp group, with the accounts team spending a full day each month decoding them before they can be coded and posted?
Bookkeeping Automation Software Development
Manual bookkeeping is not a people problem. It is a systems problem. When transactions come from five bank accounts, two payment processors, a credit card provider, and an expense platform, the bookkeeping team spends most of its time moving data between systems rather than reviewing it.
We build bookkeeping automation software that pulls transaction data from every source, categorises it against your chart of accounts, matches it to open invoices and purchase orders, and flags only the exceptions that need a human decision.
Bank feed integration pulling transactions automatically from all accounts using open banking and direct bank connections
AI-assisted transaction categorisation using rules and machine learning trained on your chart of accounts and posting history
Receipt capture via mobile app, email, and supplier portal with automatic data extraction and matching
Reconciliation automation matching bank transactions to ledger entries with exceptions routed for human review
RaftLabs builds custom bookkeeping automation software for finance teams and accounting firms -- bank feed integration, AI-assisted transaction categorisation, receipt capture, reconciliation automation, and expense management workflows. Most bookkeeping automation projects deliver in 8 to 12 weeks at a fixed, agreed cost.
100+Software products shipped
·FixedCost delivery
·8-12Week delivery cycles
·24+Industries served
When manual bookkeeping becomes the bottleneck
Bookkeeping does not scale with the business. A team that handles 200 transactions a month manages with manual processes. At 2,000 transactions a month, the same team is behind every close. At 20,000 transactions, they are always behind. The volume increase is not matched by a proportional increase in team size -- and hiring more bookkeepers to do the same manual work is not the right answer.
The automation opportunity in bookkeeping is larger than most finance teams realise. The majority of transactions in a typical business follow predictable patterns -- the same supplier, the same amount range, the same account code, the same cost centre. A well-configured rules engine categorises 70 to 85 percent of transactions without human involvement. AI-assisted categorisation handles a further 10 to 15 percent where the rules are not exact but the pattern is clear. What remains -- the new suppliers, the unusual transactions, the disputed amounts -- goes to a human reviewer who can deal with exceptions in a fraction of the time the manual process takes.
We build bookkeeping automation systems for accounting firms processing client books at scale and for in-house finance teams whose transaction volume has outgrown their current process. The automation logic is built around your specific chart of accounts, your supplier base, and your categorisation rules -- not a generic model that has to be corrected constantly.
What we build
Bank feed integration
Bank feed connections pulling transaction data automatically from all your accounts -- current accounts, savings accounts, credit cards, and merchant accounts -- using open banking APIs, direct bank data feeds, or bank file import where direct connection is not available. Multi-bank aggregation bringing transactions from different financial institutions into a single transaction queue, normalised to a consistent format regardless of how each bank formats its transaction data. Transaction deduplication detecting and flagging duplicate entries when the same transaction arrives through more than one feed -- common when a bank feed and a manual import overlap at period end. Real-time or daily feed refresh configured to your bank's update frequency, with a feed status dashboard showing which accounts are connected and when each was last updated. Feed failure alerts notifying the finance team when a bank connection drops so missing transactions are caught before they become a reconciliation problem.
AI transaction categorisation
Rules-based categorisation engine applying your defined posting rules first -- the direct debit from HMRC always goes to corporation tax, the standing order to the landlord always goes to rent, the card transaction at the specific fuel supplier always goes to vehicle costs. AI-assisted categorisation for transactions that do not match a defined rule, using a model trained on your historical posting data to suggest the account code, cost centre, and VAT treatment with a confidence score. Low-confidence suggestions routed to a human reviewer rather than posted automatically, with the reviewer's decision fed back to the model to improve future categorisation accuracy. New supplier detection flagging transactions from payees not previously seen so the finance team can set up the supplier record and define the default posting before the transaction is categorised. Categorisation accuracy reporting showing the auto-categorisation rate, the review rate, and the override rate -- the metrics that tell you whether the automation is working as intended.
Receipt capture and processing
Mobile receipt capture via a smartphone app allowing employees to photograph receipts at point of purchase, with OCR extraction of the supplier name, date, amount, and VAT amount from the image. Email receipt forwarding for digital receipts sent to a dedicated capture address, with the receipt data extracted automatically and matched to the corresponding bank transaction or credit card charge. Supplier portal for suppliers submitting invoices directly, with the invoice data extracted and routed to the AP workflow without manual re-entry. Receipt matching against bank feed transactions -- the photographed receipt matched to the bank transaction by amount and date, with the combined record showing the transaction detail and the receipt image together. Missing receipt alerts for transactions that have been categorised but have no receipt attached, sent to the employee or cardholder so they can submit the documentation before the period closes.
Reconciliation automation
Automated bank reconciliation matching bank feed transactions to posted ledger entries using a combination of amount, date, reference, and payee name matching, with configurable tolerance for date differences on transactions that clear with a delay. One-to-many and many-to-one matching for transactions that do not correspond one-to-one -- a single bank payment covering multiple invoices matched to each invoice, or multiple smaller payments matched to a single bank receipt. Unmatched transaction queue for items the system cannot match automatically, showing the bank transaction alongside the closest candidate ledger entries so the reviewer can match, create a new posting, or flag for investigation. Reconciliation completion status across all bank accounts visible on a dashboard showing the number of matched, unmatched, and in-review items per account and the percentage of the period's transactions reconciled. Statement balance confirmation comparing the system's reconciled balance to the bank statement closing balance as the final step in the reconciliation process.
Expense management
Expense claim submission for employees -- the employee logs the expense, attaches the receipt, selects the expense category, and submits for approval without leaving the mobile app or web interface. Approval workflow routing each expense claim to the employee's line manager or cost centre owner for approval before it is posted to the ledger and included in the next payment run. Company credit card reconciliation matching card transactions to submitted receipts and expense claims, with a status view showing which card transactions have been claimed and which are outstanding against each cardholder. Policy enforcement flagging claims that breach the expense policy -- a hotel rate above the policy limit, a meal claim without a business purpose, a claim submitted outside the policy time window -- before the claim reaches the approver. Payment run generation producing a BACS file or payment instruction for approved expense claims, paid in the next payment run rather than requiring individual bank transfers.
Supplier and payables integration
Supplier invoice capture via email ingestion and supplier portal, with OCR extraction of invoice header and line item data and matching against purchase orders and goods received notes for three-way matching. Duplicate invoice detection checking incoming invoices against the open invoice register and the posting history to prevent paying the same invoice twice when a supplier re-submits or a feed delivers a duplicate. AP ageing report showing all outstanding supplier invoices by age, supplier, and due date so the payment team can see what is due and approaching overdue without running a manual query. Payment approval workflow routing payment proposals above configured thresholds for authorisation before the payment run is submitted to the bank. Remittance advice generation and dispatch to suppliers on payment, with the remittance showing which invoices are covered by the payment so suppliers can allocate correctly.
Frequently asked questions
For a business with established suppliers and consistent transaction patterns, a well-configured rules engine typically categorises 70 to 85 percent of transactions automatically. AI-assisted categorisation handles a further 10 to 15 percent where the pattern is clear but the rules don't exactly match. The remainder -- new suppliers, unusual amounts, disputed transactions -- goes to a human reviewer. The exact split depends on the consistency of your transaction patterns and how well the categorisation rules are configured at the start.
We connect to UK and EU banks via open banking APIs, to major banks in the US and elsewhere via direct data feeds or OFX file import, and to payment platforms including Stripe, PayPal, Square, GoCardless, and Adyen. For banks without a direct API connection, we build automated file import from the bank's transaction export format. We confirm the connection options available for your specific banks during discovery before development starts.
Yes. We can build bookkeeping automation as a layer that feeds categorised transactions into an existing ledger -- posting to QuickBooks, Xero, Sage, or a custom general ledger via API. This approach lets you keep your current accounting platform while removing the manual data entry. Alternatively, we build the automation as part of a full custom accounting system if you are replacing the platform at the same time.
A focused build covering bank feed integration, AI-assisted categorisation, receipt capture, and reconciliation automation typically runs $25,000 to $50,000 depending on the number of bank connections and the complexity of the categorisation rules. Adding expense management, supplier invoice processing, three-way matching, and AP workflow typically brings the total to $50,000 to $90,000. Fixed cost agreed before development starts, no hourly billing.
Talk to us about your bookkeeping automation project.
Tell us your transaction volumes, your bank accounts and payment platforms, and where the manual work is concentrated. We will scope the automation layer that removes the bottleneck.