Month-end close at most finance functions is a manual, people-intensive process with no memory, no self-correction, and no leverage.
Each close starts from scratch: pulling the same reports, chasing the same reconciliations, answering the same variance questions. The controller carries the entire process in their head, which makes every close dependent on one person's bandwidth and creates no institutional learning. QuantFi ran this problem on itself before building for clients.
QuantFi built a four-layer agentic month-end close system on Claude, a knowledge base layer encoding the company's chart of accounts, accounting policies, and prior-period logic; an integration layer connected to QuickBooks Online; an autonomous agent execution layer; and a human gate for controller review.
The agent produces first-draft explanations for every line-item movement exceeding threshold, eliminating the most time-intensive manual step in a typical close.
Each approved close decision is written back into the knowledge base, so the system's accuracy and autonomy increase over time rather than resetting to zero each month.
Speed of data gathering, human approval, and execution enabled faster decisions and faster close time.
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