AI for accounting firms in Ontario.
Built around your stack.

What we’d build first

Almost every accounting practice we audit picks document processing first. The pain is loudest, the ROI is fastest, and the workflow is the textbook case for AI. Receipts, T-slips, vendor invoices, brokerage statements, the bookkeeper-of-the-month’s spreadsheet. Most of it arrives as PDFs or screenshots. Today, somebody at your firm is typing those numbers into QuickBooks, Xero, or a Caseware working paper. That work is high-volume and low-judgment.

The system we build reads the documents on intake. OCR extracts the line items. An LLM categorizes each entry against the client’s chart of accounts, matches vendors it has seen before, and posts a draft into your bookkeeping tool. Anything ambiguous gets flagged with a one-line reason. “Vendor ‘Costco Wholesale’ — possible split between meals (50 percent) and office supplies. Last year you split this 70/30. Confirm?” By the time your senior opens the file, the work is 85 percent done.

Internal reporting usually lands second, often inside the same engagement, because the data pipes are already wired up. A morning digest in your inbox by 7am: outstanding documents older than seven days, returns due this week, WIP aging, yesterday’s billable hours against target. The day already triaged before you finish the first coffee.

Built around your stack

We don’t replace QuickBooks, Xero, CCH, or Caseware. We integrate with them. The system writes drafts where your team already works, surfaces flags where your team already looks, and pulls from the tools you already pay for. You keep your file structure, your chart of accounts, your engagement workflow. We add the layer that does the typing.

That goes for the rest of your stack too. Practice management, secure file portals, Microsoft 365 or Google Workspace, your CRM, your e-signature tool. If your team uses it daily, the automation lives inside it. The goal is fewer tabs, not more.

The judgment stays yours

AI is good at extracting, categorizing, drafting, and chasing. It is not good at deciding whether a $4,800 invoice from a numbered company is a real subcontractor or a related-party transfer that needs a T5018. It is not good at deciding when to question a client versus when to code it. It is not good at year-end planning conversations or holdco strategy. That’s exactly the work a senior partner has spent a career learning.

The system flags anomalies and proposes accounts. The accountant approves. Junior staff get their billable hours back from data entry and into the work that actually develops them. The senior partner’s experience — which is the thing AI cannot replicate — is exactly where the human-in-the-loop sits. The busywork is what we automate. The judgment is what we leave alone.