AI Solution

Collections + recon

The whole receivables cycle on autopilot: ageing debtors chased on the channel they answer, at the moment they are most likely to pay, and the bank reconciliation done the day the money lands.

The cash is yours. It is just late.

Money you have already earned sits in someone else's account because nobody chased them at the right moment, on the channel they actually read. Chasing is awkward, repetitive, and easy to put off, so it slips, and a week's delay on every invoice becomes a quiet, permanent hole in your cash.

Then the money does arrive, and a second job begins. Matching inflows to invoices, including the messy cases where one payment covers many invoices and many payments clear one, is slow manual work that the accounting system gives up on. The ledger stays open longer than the debt did.

So we ran the whole cycle as one line. It chases on WhatsApp and email, timed to when each customer actually pays, and the day the money lands it reads the bank statement, matches it to the right invoices, and closes the ledger. The genuinely stuck accounts come back to your team, with the full history attached.

The architecture, in one picture

From an ageing invoice to a reconciled ledger, chased and closed on its own.

Five steps and a loop. It surfaces the debt, drafts and times the chase, reads the money in, and closes the books, then learns when each customer actually pays.

Read

Surface the ageing

Open invoices, pulled live
Sorted by how late, how much

Write

Draft the chase

Polite, in your voice
Past payment history factored in

Chase

Sent, well timed

On the channel they answer
Timed to when they pay

Recon

Read the money in

Inflows matched to invoices
One-to-many and many-to-one

Close the ledger

Invoices closed, books clean
Slow payers flagged for review

BANK

Feed

Your bank statements

Read the day money lands

How it begins

It watches what is owed, live.

It pulls open invoices straight from your accounting system and sorts them by how late and how large, continuously, so the chase list is always current without anyone exporting a thing.

Where it earns its keep

It chases the way your best collector would.

Claude drafts each reminder in your voice, polite and specific, using what that customer has paid before. It sends on WhatsApp or email, whichever they answer, and times the nudge to when they usually pay, then follows up until they do or it escalates.

The part everyone dreads

The day money lands, the books close.

It reads the bank statement, matches each inflow to the right invoices, including one payment across many invoices and many payments clearing one, and writes the closure back to your accounting system. What it cannot match with confidence comes to your team with the full trail.

The blueprint

What makes it work.

The chasing and the reconciliation are connectors into systems you already run. The AI earns its keep where the work needs judgement: writing the chase, timing it, and matching messy money to the right invoices.

Ledger read. Open invoices are pulled live and sorted by ageing band, customer, and amount, so the list is never stale. Tally Zoho SAP NetSuite
The chase, in your voice. Claude drafts each reminder polite and specific, in your tone and the customer's language, using their own payment history. Claude
Timing and channel. Each nudge goes out on the channel the customer answers, at the hour they usually pay, and follows up on a schedule until they pay or it escalates. WhatsApp Email
Reading the money in. Bank statements are parsed the day inflows land, and each payment is matched to the right invoices, including the one-to-many and many-to-one cases the accounting system gives up on. Claude
Closing and writeback. Matched invoices are closed and written back to your accounting system, and a daily report shows your finance team exactly where the cash is. Tally SAP
Beyond collecting. The same view flags who pays late every time and who to tighten terms with, so collections becomes a credit decision, not just a chase.