How a $15M services company cut month-end close from 12 days to 3
A $15M professional services firm with 45 employees eliminated 9 days from their month-end close using AI-powered bank reconciliation and automated journal entries.
3 days
Month-end close
$1,300/mo
Labor cost for close
< 0.5%
Categorization error rate
47 days
Full ROI achieved
The company
A $15M professional services firm with 45 employees, running QuickBooks Online. The finance team consisted of a controller and two bookkeepers responsible for all transaction processing, reconciliation, and month-end close activities.
The challenge
The month-end close was a 12-day marathon. Every month, two bookkeepers spent the first week manually reconciling bank transactions, categorizing expenses, and preparing journal entries. The controller spent the second week reviewing, correcting errors, and finalizing reports.
With over 1,200 transactions per month across three bank accounts and two credit cards, the bookkeeping team spent roughly 60% of their time on repetitive data entry. Categorization errors averaged 4–5% — not catastrophic, but enough to create rework during review and distort departmental P&L reports.
The controller estimated that the team spent 120+ hours per month on work that added no analytical value. At their blended labor cost, that translated to roughly $5,500/month in wasted capacity — capacity that should have been allocated to cash flow forecasting and advisory work for the firm's partners.
What we built
We deployed three AI agents inside their existing QuickBooks Online environment. The first agent handled transaction categorization — it learned the firm's chart of accounts, vendor patterns, and historical categorization decisions, then began auto-categorizing new transactions with a human-review dashboard for low-confidence items.
The second agent automated daily bank reconciliation. Instead of the bookkeepers manually matching transactions at month-end, the AI reconciled daily — flagging discrepancies within 24 hours instead of letting them accumulate for weeks.
The third agent was a close workflow manager that automated routine journal entries (accruals, prepaid amortization, intercompany allocations) and generated a close checklist that tracked completion in real time. The controller could see exactly where the close stood at any moment.
The results
Before
12 days
After
3 days
Month-end close
Before
$5,500/mo
After
$1,300/mo
Labor cost for close
Before
4–5%
After
< 0.5%
Categorization error rate
Before
N/A
After
47 days
Full ROI achieved
How much can AI automation reduce month-end close time?
In this engagement, AI-powered bank reconciliation, automated journal entries, and a close workflow agent reduced a $15M company's month-end close from 12 days to 3 — a 75% reduction — while cutting labor costs by $4,200/month and achieving full ROI in 47 days.
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