Bookkeeping Automation
Let AI suggest categories and match transactions, while your team reviews exceptions instead of keying every line. The routine is automated and the judgment stays with your people.
The problem
Categorizing transactions and reconciling accounts are repetitive and add up across many clients. The same vendor shows up month after month, yet someone still codes it by hand. Reconciliation is slightly different: it is not hard, but it demands attention, and attention is what runs short when thirty clients' books need closing in the same week. Errors creep in at exactly the points where people are tired.
How AI helps
- Suggests categories based on how you have coded similar items.
- Matches bank lines to ledger entries and flags differences.
- Learns client-specific patterns over time.
- Puts only the unusual items in front of your team.
How it works in practice
- 1
Connect the ledger
Link the client's accounting system, subject to what is confirmed for your setup.
- 2
Learn from history
The tool studies how items were coded in earlier periods, so suggestions follow your conventions.
- 3
Suggest and match
New transactions are given suggested categories, and bank lines are matched to entries.
- 4
Review exceptions
Your team looks at items the tool is unsure about, approves or changes the rest in bulk and posts the result.
A worked example (illustrative)
These numbers are made up to show the method. Use your own on a call and we will work out your real figures.
| Step | Example figure |
|---|---|
| Time per client month of books | 3 hours |
| How many a month | 25 |
| Hours a month today | 75 |
| Hours saved if about 40% is automated | 30 |
What drives the saving
Twenty-five client months at three hours is 75 hours. Saving forty percent gives 30. The figure that matters most is the percentage, and it depends on the clients. A client with a few regular vendors and clean bank feeds may save much more. A client with irregular transactions, cash handling or poor receipts may save little. Many firms find it sensible to start with their most regular clients and learn from them before widening.
Keeping accuracy high
A suggestion is not a decision. Good practice is to review the first few months of every client closely, to check a sample after that and to look at every item the tool is unsure about. Track the error rate. If the tool is wrong more often than your staff are, something needs fixing. It also helps to keep the chart of accounts tidy: if two categories overlap, suggestions will be inconsistent. A clean chart helps people as well as software.
Who owns the books
The firm and the client remain responsible for the accuracy of the records. The tool does not post anything the firm has not approved, and it does not make accounting judgments about difficult items. A reviewer should be able to see why a suggestion was made, and to override it. If a tool cannot explain itself, treat its suggestions with more caution.
Ways to get it, cheapest first
Contact us for pricing.
Level 1
Launch Kit
An accounting software kit with suggested coding.
Level 2
Ready-made agent
A ready-made agent that connects to your client's ledger.
Level 3
Custom build
A custom build tuned to your firm's chart of accounts and review steps.
Limits
The tool works best on regular, well-documented transactions. It does not give accounting or tax advice and does not replace the review a professional would give. Connections to accounting systems are confirmed for your setup, and client data should be handled under terms agreed in writing.
Suggestions are for review. A person approves what is posted.
Preparing a client's books for automation
- Tidy the chart of accounts and remove overlapping categories.
- Make sure bank feeds are connected and complete.
- Agree conventions for common vendors.
- Decide how receipts and notes are attached.
- Review the last few months, so the tool learns from good examples.
Month-end with an assistant
In a typical month, the assistant has already suggested categories and matched most lines by the time staff open the file. They look at the exceptions, check a sample of the rest, make corrections and post. The month-end review, which used to start with keying, starts with questions. That shift is where the saving comes from. It also makes quality easier to manage, because exceptions are visible rather than buried in a long list.
What to track
Track the share of items the tool categorizes correctly, the number of exceptions per client and the time to close each month. If accuracy is high and exceptions are few, extend to more clients. If accuracy is poor for one client, look at the data: it may point to a problem with that client's records rather than with the tool. Keep a simple log, and review it monthly.
Reviewing exceptions well
The exceptions list is the center of the workflow, so it deserves care. Sort it so the largest or oddest items come first. Record why an item was unusual, so patterns can be fixed upstream. If the same vendor keeps appearing as an exception, add a rule. If a client keeps sending unclear receipts, ask for better ones. Over time, the list should shrink, and what remains should be the items that truly need a person. If it does not shrink, something upstream is wrong.
Bookkeeping questions
Which systems does it work with?
We support common accounting systems, and connections are confirmed for your setup. Tell us what you use.
Does it post entries by itself?
We recommend that a person approves what is posted. You decide how much to automate.
How long before suggestions are good?
It depends on the client and the history available. We suggest a trial on a few clients first.
What about clients with poor records?
The saving will be smaller. The tool flags what it cannot match, which can itself be useful.
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