We spoke with Safebooks AI about what happens when AI can connect that context—and begin doing more of the underlying finance work itself.
For someone hearing about Double for the first time—what do you actually do, in plain English?
Safebooks uses auditable AI agents to perform work behind the financial close, including reconciliations, journal entry preparation, transaction validation, and investigations. Its Financial Data Graph™ connects information across existing systems so agents have the context to complete that work, while finance teams remain responsible for reviewing exceptions and exercising judgment.
What’s breaking (or slowing down) for finance teams today—and why is it becoming harder to ignore?
The information needed to complete even a single accounting task can be spread across several systems. Accountants may need to pull transactions from the ERP, compare them with bank or billing data, check supporting documentation, apply company policies, investigate discrepancies, and then document how they reached a conclusion.
That fragmentation is difficult to solve with traditional automation because the challenge isn’t simply moving data from one place to another—it’s understanding how the pieces relate. As transaction volumes and system complexity grow, manually reconstructing that context can consume more of the close and push important investigations toward the end of the reporting cycle.
Who tends to get the most value from this—and where does it typically click fastest?
Complex finance environments create the clearest case for this kind of approach. Mid-market and enterprise businesses operating across multiple entities, systems, business units, or high transaction volumes often have significant reconciliation and validation work that still depends on people moving between applications.
CFOs, CAOs, Controllers, and accounting leaders may own the business case, while finance operations, transformation, and systems teams often play an important role in determining where agents can take on execution without compromising governance or existing controls.
If a finance team started using your approach tomorrow, where would they feel the impact first?
Workflows that require the same pattern of gathering, comparing, investigating, and documenting information are natural starting points. Cross-system reconciliations, transaction validation, journal entry preparation, variance investigations, and control checks all involve significant preparation before an accountant can make a judgment.
With agents handling more of that groundwork, the accountant’s starting point changes. Instead of assembling the evidence and searching for discrepancies, teams can begin with work that has already been prepared and focus their attention on the exceptions that actually require review.
How does this fit into the existing finance tech stack—and how does it hold up as the business grows?
Rather than introducing another system of record, Safebooks works across the technology a company already uses, including ERPs, CRMs, billing platforms, banks, payment systems, data warehouses, and other business applications. The Financial Data Graph™ brings relevant records, documents, policies, and relationships together so agents can execute workflows using context from multiple sources.
As new entities, systems, and processes enter the picture, that same foundation can support additional agent-driven workflows instead of requiring a separate automation tool for each one. The longer-term idea is a shift toward continuous financial operations, where reconciliations, validations, and investigations happen as activity occurs—not only when the calendar reaches month-end.
Explore More at the Finance & Accounting Technology Expo (FATE) 2026
Meet Safebooks AI at FATE 2026 (Nov 18–19, NYC)—a curated, high-signal environment where finance teams can compare solutions side-by-side, see how real workflows operate, and evaluate what fits their business.
