AI bookkeeping automation setup

Setup covers a full read of your existing bank, card and payment-processor feeds to identify which transactions repeat in a predictable pattern and which do not. From there we build categorization rules against your actual chart of accounts, not a template pulled from a different industry, and connect receipt and bill capture through Dext, Hubdoc or your current inbox workflow.

A confidence threshold gets set for how certain the model needs to be before a transaction posts without review, and anything below that line routes to a named person on your delivery pod. We also set up a written change log so every rule edit, whether it is a new vendor mapping or an adjusted threshold, has a record explaining what changed and why. The exact scope, including which feeds and how many rules, is confirmed in writing before setup starts.

We start with 60 to 90 days of your existing transaction history, long enough to see real repeat patterns without leaning on a single unusual month. From that history we draft an initial rule set and categorization model, then run it in a shadow pass against a recent closed period to see how it would have coded that month compared with what actually happened.

Any mismatch gets reviewed with your bookkeeping lead before the rules go live, so the first real month is not the first time anyone checks the model's judgment. Once live, every transaction still gets a confidence score. High-confidence items post; low-confidence items land on a review queue that works the same way an open-items list already does in a normal monthly close. We revisit thresholds after the first two or three closes once there is enough real data to know where the model is reliable and where it is not.

This fits businesses with enough repeat transaction volume that manual coding has become the bottleneck in the close, typically once monthly transaction counts run into the hundreds: an e-commerce brand with daily marketplace payouts, a SaaS company billing through Stripe, or a services firm with a stable roster of recurring vendors and subscriptions.

It is a poor fit for a business with genuinely low or highly irregular transaction volume, where building and maintaining a model costs more in setup time than it saves in review time. We tell you that up front during scoping rather than selling a setup that will not pay for itself.

The most common issue in an existing setup is a categorization model or rule set that was built once and never revisited, so it silently miscodes new vendors or new revenue streams that did not exist when the rules were written. We also regularly find no real confidence threshold at all: software auto-posts everything, and nobody is checking what it got wrong until an outside accountant or the IRS does.

A third common gap is receipt capture running with no connection back to the categorization logic, so a picture of a receipt sits in Dext or Hubdoc unmatched while the bank transaction posts uncoded anyway. We rebuild the connection so capture and coding work together instead of as two disconnected steps someone has to reconcile by hand later.

Setup runs inside QuickBooks Online or Xero under your own subscription, so the rules and history stay yours if the engagement ever ends. Dext and Hubdoc handle receipt and bill capture, matching images and PDFs to bank and card transactions. Ramp extends categorization to the point of purchase for card spend, applying merchant-category logic before a transaction even reaches the bookkeeping feed.

We do not require a proprietary platform on top of what you already use. If your existing QuickBooks Online or Xero rules are reasonable, they get kept and extended rather than replaced from scratch.

AI bookkeeping automation setup is a project-based engagement priced by transaction volume, number of feeds and how much rule-building the existing books need, separate from the ongoing monthly bookkeeping fee on the published rate card. A business already on a Finbryn bookkeeping tier gets setup scoped against what that tier already covers so nothing is double-billed.

The rate card at /us/pricing lists the ongoing bookkeeping tiers this setup runs on top of; the setup project itself is quoted after a scoping call once we have seen the actual feeds and transaction history.

Quality is tracked against two numbers: how much of your monthly transaction volume the model codes correctly without review, and how often a reviewed item turns out to need a rule fix rather than a one-off correction. Both numbers get reported after each of the first several closes so you can see the automation actually improving, not just taking our word for it.

A rising review rate on a category that used to be stable is treated as a signal something changed in the business, a new vendor, a new revenue line, before it is treated as a model problem. Either way, it gets a written explanation, not a silent adjustment.

The categorization model is trained on your own transaction history and used for your books only; it is not pooled with other clients' data or used to build a shared model across engagements. Nothing the model proposes posts to your ledger without either falling above the agreed confidence threshold with human spot checks, or being explicitly approved by your reviewer.

Access to the rule set and change log is limited to your delivery pod, and you can request the full history of rule changes at any time. If the engagement ends, the underlying QuickBooks Online or Xero rules stay in your account; only the setup documentation and internal tuning notes stay with us.