AI Mind Engine · For accounting firms
It reconciles, ties out, and preps the close, then flags only what it can't certify—so your team reviews the exceptions, not everything. And it runs on confidential compute: no third-party AI, no training on your clients' data. Start from a spreadsheet.
AI tools will draft the whole close—but you still review 100% of it, because nothing tells you which numbers to trust. The labor you wanted to save is now spent checking a machine.
Uploading a client’s ledgers, tax forms, or P&Ls to cloud AI breaks confidentiality and SOC2—and consumer models train on what you send. So the most sensitive, highest-value work stays manual.
Categorization automation misses the judgment calls—gross vs. net, duplicate refunds, deferred revenue—and can’t prove where a number came from. Tie-outs and the close need work that arrives already checked.
Give it the bank statement and the ledger. The coworker composes exact, deterministic tools—not free-form guessing—to reconcile on a shared key, surface only-in-bank / only-in-ledger exceptions and amount mismatches, tie to the bank, and prep the month-end close.
Every figure traces to the exact tool that produced it, and a per-step trust signal reads the model’s own state to catch looks-right-but-wrong work. Your team reviews the exceptions it flags—not every line.
No third-party LLM API, nothing leaves your firm, nothing trains on your clients’ data. Verified numbers roll up to a director view—the only figures in the building checked before they’re signed off.
0.21 → 1.00
Reconciliation accuracy, once it uses exact tools
Flags, not guesses
It escalates uncertain judgment calls instead of shipping them
100%
Every number traceable to the tool that produced it
0
Client records sent to any third-party AI
A chatbot answers. A coworker does the reconciliation, checks it, shows you exactly how it got there—and keeps every client record inside your firm.
Bank-to-ledger matching on a shared key, only-in-bank / only-in-ledger exceptions and amount mismatches surfaced, tied to the bank—verified line by line.
Balances, burn, and the recurring close schedule—drafted with every number traceable to its source, flagged where a judgment call is uncertain.
Workpaper substantiation and return-prep support—the same two guarantees on your most sensitive, highest-liability engagements.
Confidential compute, single-tenant. No third-party AI, no training on client data. Start from a spreadsheet or export—no integration, no migration.
What makes it safe to put in front of a client's books: it certifies its own work, and it keeps that work inside your firm.
Every number comes from an exact, deterministic tool, not free-form reasoning—which took reconciliation accuracy from 0.21 to 1.00 in validation, each figure tracing to the tool that produced it. On top, a per-step trust signal reads the model's own internal state to catch looks-right-but-wrong work and escalate it to you rather than shipping it—a gate we hold to a live held-out canary and keep hardening before we rely on it.
The coworker runs on confidential, single-tenant compute on an open model we control—no OpenAI or Anthropic API, no third party in the loop, and nothing trains on your clients' data. The ledgers, statements, and returns that cloud AI tools can't touch under CPA confidentiality and SOC2 are exactly the work it's built to do, without any of it leaving your boundary.
Tell us the reconciliation you'd hand off first—we'll show you it verified, on your data, inside your environment.