AI driven reconciliation & data workflows
We use AI to match, clean, and reconcile the high volume data that used to take a person days, with a qualified person approving the result before it reaches your books.
The difference
Say a tool reads 98% of an invoice correctly. That's still not a 98% correct set of books, because the errors that land on an amount are the expensive ones. The machine does the matching. A person approves before anything is booked.
Matched at volume, the exception goes to a person
Before you hand anything over
How we handle your data
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Your data doesn't train anyone's model.
We run on enterprise AI services whose contractual position is that your data and prompts are never used to train their models, and we'll send you that documentation rather than ask you to take our word for it.
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Access is requested, never assumed.
Read only wherever the work allows it, asked for per system rather than granted in bulk, and named per person. Credentials are never shared, and your records never sit on personal devices.
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Preparing stays separate from approving.
A second person reviews before anything is filed or closed. On payables we prepare the run and you're the one who releases it.
Who it’s for
Businesses with enough transactions, invoices, or records that matching them by hand has become somebody's full time job. If month end is mostly two people comparing two lists, this is the work.
How we help
- 01 We start with the matching and checking that's eating the most hours, and leave the rest alone.
- 02 AI reads and matches the routine cases, which is most of them.
- 03 Anything the system isn't confident about routes to a person automatically, so review effort lands where the risk is.
- 04 A qualified person approves before anything is posted, paid, or filed.
- 05 Every step is logged, so you can show how a figure got where it is.
What you get
- Days of manual matching removed from month end.
- Exceptions surfaced early instead of found in an audit.
- An audit trail behind every automated decision.
- Cleaner data going into your reporting.
- A process that holds up as volume grows.
What owners ask about AI driven reconciliation & data workflows
Can AI actually do bank reconciliation?
It can do most of it. The routine matches, where one payment lines up with one invoice, are well within reach and they're the bulk of the volume. What it handles badly is the messy end: a single bank line covering three invoices, a payment short by a fee, a reference a customer typed wrong. Those route to a person, which is why the honest answer isn't full automation. Where the volume doesn't justify automating at all, we simply do the reconciliation itself.
Which parts stay human?
Anything that takes a judgment call, and anything expensive to get wrong. Approving a payment, deciding how an unusual transaction is treated, signing off the month end close, and every exception the system flagged. The machine proposes. A person disposes.
How do I know it got it right?
Because nothing posts on the machine's confidence alone. Each automated match carries a confidence score, and anything below the threshold we agree with you goes to review rather than through. The log shows what was matched automatically, what a person changed, and when.
What happens to the items it cannot match?
They go on an exception list with the reason attached, and a person works through them. That list earns its keep on its own, because the same exception turning up every month usually points at something upstream worth fixing.
Do we have to move off QuickBooks or Xero?
No. Your accounting platform stays your source of truth. The work happens around it and the results land in it, so you aren't being asked to swap out the one system your accountant, your lender, and your tax filing all depend on.
Further reading
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