For the modern CFO,the hardest part of the job frequently enough isn’t the math—its 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 thes agents marks a basic shift in how the “Office of the CFO” interacts with data.
Beyond the chatbot
The 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 institution 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 finance
The launch taps into a broader trend in software advancement 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 in
