QuickBooks · Accounting
Custom AI automation for QuickBooks
The hours in bookkeeping aren't in the judgment — they're in the typing, the chasing, and the tying-out. We automate those and leave the judgment to you.
Bills arrive as PDFs and get keyed in by hand. Invoices go out and then somebody has to remember to chase them. Month-end turns into a week of tying out accounts and assembling the same reports in the same order. None of it is difficult work — it's just relentless, and it scales linearly with how many clients or entities you carry.
Crystal AI automates that layer on top of the QuickBooks you already use. Documents get read and coded, receivables get chased on schedule, reconciliation gets prepared before you sit down to it, and reporting assembles itself. Everything that touches the books is proposed for review rather than posted silently — you approve, the agent executes.
How QuickBooks and Lattice connect
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What moves
- Bills and receipts
- Invoices and overdue receivables
- Transactions to reconcile
- Ledger data for month-end reporting
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What the agent does
- Reads and codes bills against your chart of accounts
- Chases overdue invoices on a schedule
- Prepares reconciliation and isolates the exceptions
- Assembles month-end packages in your template
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Where a person decides
- Nothing posts to the books without a person approving it
- Anything ambiguous is routed to a person, not guessed at
- Disputed amounts and quiet clients are flagged, not nagged
Built against the QuickBooks Online API with OAuth and least-privilege scopes. QuickBooks Desktop works differently, through a sync layer or a scheduled export.
What we automate on QuickBooks
Bill and receipt intake with coding
Incoming bills and receipts are read, matched to the right vendor and account, coded against your chart of accounts, and queued for approval — so accounts payable becomes a review queue instead of a data-entry queue.
Receivables follow-up that actually happens
Overdue invoices get chased on a schedule with escalating, appropriately-worded reminders, and anything unusual — a disputed amount, a client who's gone quiet — is flagged to a person rather than nagged automatically.
Reconciliation prep before you sit down
Transactions matched, likely pairings proposed, and the genuine exceptions isolated ahead of time, so reconciliation becomes reviewing a short list of oddities instead of working through everything line by line.
Month-end reporting and client packages
Recurring client reports and financial statement packages assembled from live QuickBooks data, formatted to your firm's template, and ready for review the moment the books close.
What you get
- Data entry becomes review — you approve rather than type
- Receivables get chased consistently without anyone remembering to do it
- Month-end close gets shorter because the prep is already done
- Capacity to take on more clients without adding headcount
- Nothing posts to the books without a human approving it first
How we build it
We build against the QuickBooks Online API using OAuth with least-privilege scopes, and every write path is designed as propose-then-approve rather than post-silently — anything touching the ledger waits for a person. Document reading is validated against known vendors and your chart of accounts, with confidence thresholds that route anything ambiguous to a human instead of guessing. Your client data is never used to train outside models, we work under an NDA and DPA, and multi-entity firms get isolation between clients by design. Most first builds are typically live within one to two weeks, with full source code and documentation at handover.
Frequently asked questions
The clean path is QuickBooks Online, which has a proper API. Desktop is possible but works differently — usually through a sync layer or scheduled export — and we'll be straight with you on the trade-offs before you commit to anything.
No. Anything that touches the ledger is proposed for approval, not posted silently. You review and approve; the agent executes. That's a deliberate design choice, and it stays that way unless you explicitly decide otherwise for a specific low-risk path.
Good, and — more importantly — honest about when it isn't. Coding is validated against your chart of accounts and known vendor history, with a confidence threshold: anything ambiguous is routed to a person rather than guessed at. You'll see the exceptions, not discover them at year-end.
Work happens under an NDA and DPA with least-privilege access, your data is never used to train outside models, and multi-entity firms get isolation between clients by design.
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