A 40-Client Accounting Firm Cut Monthly Close From 9 Days to 4
Manual invoice entry and reconciliation across dozens of client books was pushing this firm's monthly close well past client expectations. An AI document processing workflow got it under a week.
Nine days to close a month is not unusual for a firm this size doing manual entry, but it was becoming a competitive problem as newer, tech-forward firms started pitching this firm’s own clients on faster turnaround.
Where the nine days went
We audited the close process for three client accounts of varying complexity. Data entry, not analysis, was the bottleneck. Bookkeepers spent the first several days of every close just getting invoices and receipts keyed in and coded before they could even start reconciling, let alone reviewing anomalies.
Building around the existing chart of accounts
Rather than pushing clients onto a standardized coding structure, which the firm had rightly resisted for years, we built the extraction and coding workflow to map to each client’s existing chart of accounts individually. It took roughly two weeks of tuning per client cohort to get first-pass coding accuracy to a level bookkeepers trusted.
Review, not re-entry
The workflow doesn’t post anything automatically. Every extracted and coded transaction lands in a review queue where a bookkeeper approves it or corrects the coding in seconds rather than typing the entry from scratch. That distinction, review versus re-entry, was what got the firm’s bookkeepers on board quickly instead of treating it as a threat to their role.
Results
Average monthly close time across all 40 client accounts fell from 9 business days to 4. The firm has since used the freed-up time to take on 6 additional clients without adding headcount, and the retainer now covers onboarding new clients’ charts of accounts into the workflow as the roster grows.
Does this replace the bookkeepers doing the work?
No. Every transaction the system codes is queued for a bookkeeper to review and approve before it posts. The automation removed the typing, not the judgment call on how something should be categorized.
How accurate is the automatic coding compared to manual entry?
After an initial two-week tuning period against each client's chart of accounts, first-pass coding accuracy reached 94%. The remaining 6% are flagged for bookkeeper review rather than posted automatically, so nothing incorrect reaches the books unreviewed.
Can it handle clients with unusual or highly custom charts of accounts?
Yes, though it takes a short calibration period per client. We map the workflow to each client's existing chart of accounts individually rather than using one generic template across all 40, which is what keeps the coding accuracy high.