AI agents for finance operations with human sign-off

The service covers three drafting task types by default, and any of them can be scoped in or left out depending on what fits your operation. Vendor query drafting reads an incoming email or ticket, pulls the relevant invoice, payment and PO history, and proposes a reply a person edits or sends as-is. Reconciliation match drafting proposes a pairing between a bank or card transaction and an open invoice, bill or expense report, with the confidence and the reasoning behind the match shown alongside it, not just the answer. Collections outreach drafting proposes a follow-up message for an overdue balance, pulling the actual invoice number, amount and days-overdue figure into the draft rather than a generic template. Every draft carries the source records it was built from, so a reviewer is checking work, not guessing at where a number came from. A rejected or edited draft feeds back into how the next similar draft gets written for that vendor or task type.

Setup starts with a scoping conversation covering which task types, which accounts, and who the approver is on each one. We connect to the systems that already hold the source data, typically your accounting platform, your AP or collections tool, and whatever inbox or ticketing system vendor and customer messages actually arrive in, rather than asking you to move communication into a new tool. Early weeks run on a narrow scope deliberately, often a single vendor segment or a single task type, so the approval-rate data means something before scope grows. Every draft, every edit, and every rejection is logged with a timestamp and the reviewer's name, which becomes the record used both to tune the agent and to answer any question later about why a specific message went out the way it did. Reviewers see drafts inside the tool they already use for that task, not a separate dashboard they have to remember to check.

This fits a business with enough vendor or customer volume that a person is already spending real hours a week on repetitive replies, matching, or collections follow-ups, and where that volume is expected to keep growing. It is a poor fit for a business with a handful of vendors and customers, where the repetition that makes drafting valuable simply is not there yet, and standard bill pay or ar ageing support covers the need at lower cost. It also assumes you are comfortable with a named person on the delivery team, or a named person on your side, being the one who reviews and approves drafts; if you want zero involvement from anyone on the automation side, a fully manual process or a simpler rules-based tool may suit better. SaaS companies with high-volume dunning, and marketplace or agency businesses fielding repetitive vendor status questions, are the most common fit.

Vendor and customer replies that used to sit for two or three days waiting for someone to have a free hour now have a drafted first pass within the same day, so the person reviewing is editing, not starting from a blank page. Reconciliation backlogs that used to build up during a busy week get proposed matches waiting in a queue instead of an unmatched pile someone has to start from zero. Collections follow-ups that used to slip because nobody remembered which account was at day 30 versus day 60 now get drafted on schedule, so the follow-up cadence does not depend on someone's memory. And the inconsistent tone problem, where one team member's collections message reads very differently from another's, gets addressed because every draft starts from the same base pattern before a person edits it to fit the specific situation.

The agent layer works inside the accounting and communication tools already in place rather than requiring a new platform. On the accounting side that typically means QuickBooks Online, Xero or NetSuite for the underlying invoice, bill and payment data. On the communication side it depends on where vendor and customer messages already arrive, commonly a shared inbox, Zendesk, or a ticketing tool connected to your CRM. Collections and cash application drafts connect directly to the accounts receivable data already being tracked through ar ageing and cash application, so a draft reflects the actual current balance rather than a stale export. Slack or email is used for the reviewer notification that a draft is waiting, since that is almost always where the reviewer already works.

The only metric that matters here is the approval rate: what share of drafts for a given task type get approved as-is or with only a minor edit, versus rejected outright or heavily rewritten. That number is tracked per task type and per vendor or customer segment, not as a single blended figure that hides where it is actually working. A task type that stays below an agreed approval threshold after a reasonable number of drafts gets narrowed or paused rather than pushed forward on a schedule regardless of the data. We review this with you on a set cadence, typically monthly, alongside the rest of your close and reporting review, so it is one conversation about how the finance function is running, not a separate automation report nobody reads.

Pricing for this service is scoped to the task types in play and the volume of drafts they generate each month, not billed as a flat automation fee layered on top of your existing plan. It is typically added as a scoped line to a Growth or Scale engagement on our pricing page, since the drafting work draws on the same accounts, records and reviewers already covered there. A scoping call sets the actual number based on how many vendor queries, reconciliation items or collections accounts are realistically in scope in a typical month, and that scope is revisited if volume changes materially in either direction.

The line is drawn at authority, not at task complexity. An agent can draft something complicated and a person can approve it quickly if the draft is clearly right; an agent can draft something simple and a person can still reject it if the specific situation calls for judgment the model does not have. What never changes is that the agent has no authority to post an entry, send a message or execute a payment on its own. That boundary is documented for every client under AI policy and controls for accounting, and it is the same boundary a controller or a CFO would set for a new, unproven junior team member: useful output fast, real judgment kept with a named person who is accountable for what actually happens.