For the modern CFO, the hardest part of the job often isn't the math—it's the storytelling. After the books are closed and the variances calculated, finance teams spend days, sometimes weeks, manually copy-pasting charts into PowerPoint slides to explain why the numbers moved.Today, 11-year-old Israeli fintech company Datarails announced a set of new generative AI tools designed to automate that "last mile" of financial reporting, effectively allowing finance leaders to "vibe code" their way to a board deck.Launching today to accompany the firm's newly announced $70 million Series C funding round, the company’s new Strategy, Planning, and Reporting AI Finance Agents promise to answer complex financial questions with fully formatted assets, not just text. A finance professional can now ask, "What’s driving our profitability changes this year?" or "Why did Marketing go over budget last month?" and the system will instantly generate board-ready PowerPoint slides, PDF reports, or Excel files containing the answer.The deployment of these agents marks a fundamental shift in how the "Office of the CFO" interacts with data.Beyond the chatbotThe promise of the new agents is to solve the fragmentation problem that plagues finance departments. Unlike a sales leader who lives in Salesforce, or a CIO who relies on ServiceNow, the CFO has no single "system of truth". Data is scattered across ERPs, HRIS, CRMs, and bank portals.A major barrier to AI adoption in finance has been security. CFOs are rightfully hesitant to plug P&L data into public models.Datarails has addressed this by leveraging Microsoft’s Azure OpenAI Service. "We use the OpenAI in Azure to ensure the privacy and the security for our customers, they don't like to share the data in [an] open LLM," Gurfinkel noted. This allows the platform to utilize state-of-the-art models while keeping data within a secure enterprise perimeter.Datarails’ new agents sit on top of a unified data layer that connects these disparate systems. Because the AI is grounded in the company’s own unified internal data, it avoids the hallucinations common in generic LLMs while offering a level of privacy required for sensitive financial data."If the CFO wants to leverage AI on the CFO level or the organization data, they need to consolidate the data," explained Datarails CEO and co-founder Didi Gurfinkel in an interview with VentureBeat.By solving that consolidation problem first, Datarails can now offer agents that understand the context of the business. "Now the CFO can use our agents to run analysis, get insights, create reports... because now the data is ready," Gurfinkel said.'Vibe coding' for financeThe launch taps into a broader trend in software development where natural language prompts replace complex coding or manual configuration—a concept tech circles refer to as "vibe coding." Gurfinkel believes this is the future of financial engineering."Very soon, the CFO and the financial team themselves will be able to develop applications," Gurfinkel predicted. "The LLMs become so strong that in one prompt, they can replace full product runs."He described a workflow where a user could simply prompt: "That was my budget and my actual of the past year. Now build me the budget for the next year."The new agents are designed to handle exactly these types of complex, multi-variable scenarios. For example, a user could ask, "What happens if revenue grows slower next quarter?" and receive a scenario analysis in return.Because the output can be delivered as an Excel file, finance teams can verify the formulas and assumptions, maintaining the audit trail that generic AI tools often lack.Ease of adoption: The 'anti-implementation'For most engineering teams, the arrival of a new enterprise financial platform signals a looming headache: months of data migration, schema redesigns, and the inevitable friction of forcing non-technical users to abandon their preferred workflows. Datarails has engineered its way around this friction by building what might be best described as an "anti-implementation."Instead of demanding a "rip and replace" of legacy systems, the platform accepts the messy reality of the modern finance stack. The architecture is designed to decouple the data storage from the presentation layer, effectively treating the organization's existing Excel files as a frontend interface while Datarails acts as the backend database."We are not replacing anything," Gurfinkel explained. "The implementation can be very fast, from a few hours to maybe a few days".From a technical perspective, this means the "engineering" requirement is almost entirely stripped away. There are no ETL pipelines to build or Python scripts to maintain. The system comes pre-wired with over 200 native connectors—linking directly to ERPs like NetSuite and Sage, CRMs like Salesforce, and various HRIS and bank portals.The heavy lifting is replaced by a "no-code" mapping process. A finance analyst, not a developer, maps the fields from their General Ledger to their Excel models in a self-service workflow. For modules like Month-End Close, the company explicitly promises that "no IT support is needed," a phrase that likely comes as a relief to stretched CTOs. Even complex setups, such as the new Cash Management module which requires banking integrations, are typically fully operational within two to three weeks.The result is a system where the "technical debt" usually associated with financial transformation is rendered obsolete. The finance team gets their "single source of truth" without ever asking engineering to provision a database.From version Control to vision control: a pivot that paid offDatarails wasn't always the "FinanceOS" for the AI era. Founded in 2015 by Gurfinkel alongside co-founders Eyal Cohen (COO) and Oded Har-Tal (CTO), the Tel Aviv-based startup spent its early years tackling a dryer problem: version control for Excel. The initial premise was to synchronize and manage spreadsheets across enterprises, but adoption was sluggish as the team struggled to find the right product-market fit.The breakthrough came in 2020 with a strategic pivot. The team realized that finance professionals didn't want to replace Excel with a new dashboard; they wanted to fix Excel's limitations—specifically manual consolidation and data fragmentation. By shifting focus to SMB finance teams and embracing an "Excel-native" automation philosophy, the company found its stride.This alignment led to rapid scaling, fueled by a $55 million Series A in June 2021 led by Zeev Ventures, followed quickly by a $50 million Series B in March 2022 led by Qumra Capital. While the company faced headwinds during the tech downturn—resulting in an 18% workforce reduction in late 2022—it has since rebounded aggressively. By 2025, Datarails had nearly doubled its workforce to over 400 employees globally, driven by a multi-product expansion strategy that now includes Month-End Close and Cash Management solutions.Fueling the expansionThe new AI capabilities are supported by the $70 million Series C injection from One Peak, along with existing investors Vertex Growth, Vintage Investment Partners, and others. The funding arrives after a year of 70% revenue growth for Datarails, driven largely by the expansion of its product suite.More than 50% of the company's growth in 2025 came from solutions launched in the last 12 months, including Datarails Month-End Close (a tool for automating reconciliations and workflow management) and Datarails Cash Management (for real-time liquidity monitoring).These products serve as the "plumbing" that makes the new AI agents effective. By automating the month-end close and unifying cash data, Datarails ensures that when a CFO asks the AI a question, the underlying numbers are accurate and up-to-date.For Gurfinkel, the goal is to make the finance office "AI-native" without forcing users to abandon their favorite tool: Excel."We are not replacing anything," Gurfinkel said. "We connect the Excel so Excel now becomes the calculation and the presentation."With the launch of these new agents, Datarails is betting that the future of finance isn't about learning new software, but about having a conversation with the data you already have.