This post. An illustrative worked example, not a client result. The multi-entity professional-services group and its numbers are assumptions. An AI assistant matches bank-to-ledger lines, a person reviews the exceptions, and nothing posts without sign-off.
For illustration, picture a financial controller at a multi-entity services group. Two people run the close. Every month the first working days go on tying out bank statements against the ledger, entity by entity, before anyone can look at a variance. Most of that time is matching lines by hand. This is an assumed team, not a real one.
The design is a reconciliation assistant that auto-matches the bank-to-ledger lines it is confident about and flags the rest for a human. Nothing posts without sign-off, and the full audit trail is retained. This post shows what would change for the finance team. The pattern maps to the reconciliation use case at /ai-for/finance and to workflow automation under AI implementation.
#What would change for the two-person close team?
The manual tie-out goes away. A person no longer scrolls two statements side by side looking for the line that doesn't agree. The assistant reads the bank feed and the ledger for each entity, matches what it can, and hands back a short list of exceptions. The two people who used to spend most of the close matching now review the exceptions and read the variances. How much time that frees is the number to measure on your own close.
How does AI for month-end close actually work?
An AI assistant reads each entity's bank feed and ledger, auto-matches the lines it is confident about, and flags the rest for a person to review. Nothing posts without human sign-off, and a full audit trail is retained. The team spends the freed time on variance analysis, not tie-outs.
#Does a human still sign off before anything posts?
Yes. Every posting is approved by a person. The assistant matches and proposes. It does not post on its own. The lines it can't confidently match are the ones a human looks at first, because those are where the real judgment lives. The audit trail records what matched automatically, what a person changed, and who approved it. An auditor can trace any line back to the source.
- The assistant auto-matches the bank-to-ledger lines it is confident about, per entity
- The remaining lines are flagged as exceptions for a person to review
- Nothing posts without human sign-off; the full audit trail is retained
- Close time, measured before and after, is the number to track
- The freed time moves to variance analysis
We are
We design for a result you can measure, close days, with a person approving every posting and a full audit trail retained.
We aren't
We are not selling an autonomous close bot that posts journal entries on its own and asks your controller to trust it.
#Why a worked example, and why close days as the metric?
We have no client close result to publish, and we will not invent one. The team and the numbers here are assumptions, so you can test the logic against your own close. Most finance write-ups focus on time-to-pay, like the invoice-processing pattern in the journal. Close days are a different metric for a different job: a controller whose close eats the first week of the month wants to see close days move.
If you run a close and the first week of every month disappears into tie-outs, that is the shape of problem this fits. We would rather show you an honest worked example than a pitch. tell us what you're building and we will tell you whether close days are the right metric to move first.
