Datarails’ AI Finance Agents Promise ‘Vibe Coding’ for CFOs, Not Another Dashboard

For many CFOs, the real bottleneck in reporting is no longer closing the books—it’s turning numbers into a narrative stakeholders can act on. Days or weeks can vanish as finance teams move charts and tables into slides, rework explanations, and iterate on the “story” behind the variances. Israeli fintech company Datarails is now positioning generative AI as a way to automate that last mile, letting finance leaders describe what they need in natural language and get back board-ready outputs.

Alongside a new $70 million Series C funding round, Datarails has launched a suite of Strategy, Planning, and Reporting AI Finance Agents. These agents sit on top of the company’s unified finance data layer and promise to answer complex questions—like what’s driving profitability changes or why a department overspent—by generating complete PowerPoint decks, PDF reports, or Excel files rather than just chat-style text responses.

For CFOs and FP&A leaders, the pitch is straightforward: keep Excel, fix data fragmentation, and gain an AI “front end” that responds to prompts with analysis and formatted deliverables, without building pipelines or new dashboards.

From manual storytelling to AI-generated board decks

A recurring pain point in modern finance functions is the heavy manual work that begins after the numbers are technically “done.” Once variances are calculated and books are closed, teams often spend a disproportionate amount of time packaging insights: copying charts into slide templates, reformatting tables, and writing commentary to explain what changed and why.

Datarails’ new AI Finance Agents are designed to target this post-close storytelling phase. Instead of slicing data manually and rebuilding narratives for each audience, a finance professional can now ask questions in plain language—such as “What’s driving our profitability changes this year?” or “Why did Marketing go over budget last month?”—and receive a ready-to-use deliverable.

Crucially, the outputs are not limited to paragraphs of explanation. The agents can generate complete board-ready PowerPoint slides, detailed PDF reports, or structured Excel workbooks. For a CFO preparing for a quarterly board meeting, that could mean moving from a list of questions to a working deck in minutes, with the supporting data already wired in.

Because the underlying models are grounded in the organization’s consolidated internal data, the intent is that the AI is not simply drafting generic language but building explanations tied to the company’s real ledgers, budgets, and operational data. The “last mile” of reporting—translating figures into a coherent, visual story—is where Datarails believes generative AI can provide the most immediate leverage.

How Datarails’ agents work under the hood

The promise of AI-generated reports is only as strong as the data foundation beneath it. Unlike functions such as sales or IT that often operate from a single dominant system (Salesforce, ServiceNow, and so on), CFOs typically navigate a fragmented landscape: ERP systems, HRIS, CRMs, bank portals, and dozens of spreadsheets.

Datarails tackles this fragmentation with a unified data layer that pulls together these disparate sources. According to company co-founder and CEO Didi Gurfinkel, the prerequisite for meaningful AI at the CFO level is consolidation: the organization’s financial and operational data must first be harmonized into a single, consistent view.

On top of that layer, the company deploys its AI agents using Microsoft’s Azure OpenAI Service. This matters for two reasons that tend to be top of mind for CFOs and CTOs alike:

• Security and privacy: Sensitive P&L and cash data is not sent to public, open large language models. Instead, Azure OpenAI is used to keep processing within an enterprise-grade environment, which is intended to satisfy stricter privacy and security expectations.
• Grounded answers: Because the models operate against the company’s own unified data rather than the open internet, Datarails aims to minimize hallucinations—a common concern with generic LLM tools that may produce plausible but incorrect figures or explanations.

Once the data is consolidated and mapped, the agents can execute tasks such as variance analysis, budget vs. actuals reviews, and scenario modeling, then package the outputs into the formats finance teams already rely on. As Gurfinkel describes it, “Now the CFO can use our agents to run analysis, get insights, create reports… because now the data is ready.”

‘Vibe coding’ for finance: prompts instead of models and macros

In software circles, “vibe coding” has become shorthand for using natural-language prompts in place of writing detailed code or configuration. Datarails is explicitly framing its new agents as bringing that paradigm to the Office of the CFO.

Instead of building complex workbooks from scratch or wiring up new models every time the plan changes, the idea is that a finance user can simply describe the objective and constraints. For example:

• “That was my budget and my actual of the past year. Now build me the budget for the next year.”
• “What happens if revenue grows slower next quarter?”

The agents are meant to handle multi-variable, multi-period scenarios like these. In practice, this means the system can produce a scenario analysis showing the impact of slower revenue growth, then return the results as an Excel file that the team can inspect.

That Excel output is important from a control and governance standpoint. Finance teams can review formulas, trace assumptions, and maintain the audit trail they are accustomed to, rather than treating the AI’s answer as a black box. For CFOs considering how far to push AI into planning and reporting workflows, this compatibility with existing validation practices is likely to be a key factor.

Gurfinkel goes further, suggesting that as models improve, finance professionals themselves will be able to “develop applications” with a single prompt—effectively asking the system to orchestrate end-to-end workflows that once required dedicated products or custom builds. While the long-term extent of that shift remains to be seen, Datarails is clearly betting that prompt-driven “vibe coding” will become a mainstream way of working in finance.

Implementation without a rip-and-replace

For many organizations, the barrier to adopting a new finance platform is not interest, but implementation. Traditional deployments can require months of engineering support: building ETL pipelines, redesigning schemas, and training non-technical users on new interfaces. Datarails is positioning its architecture as an “anti-implementation” approach aimed at sidestepping that friction.

Rather than asking companies to abandon legacy systems or retire Excel, Datarails treats existing spreadsheets as a familiar front end and uses its own platform as the backend database. In other words, data storage is decoupled from the presentation layer: Excel remains the canvas where analysts work, but the numbers and structures are served and governed centrally.

According to Gurfinkel, “We are not replacing anything,” and implementations can often be completed in a matter of hours to a few days for many workflows. Technically, this is enabled by several elements:

• Native connectors: Datarails reports more than 200 out-of-the-box integrations to ERPs like NetSuite and Sage, CRMs like Salesforce, HR systems, and bank portals. This reduces the need for custom data plumbing.
• No-code mapping: A finance analyst—not an engineer—maps the chart of accounts and other key fields from the General Ledger into existing Excel models using a self-service interface. For many modules, including Month-End Close, the company explicitly states that “no IT support is needed.”
• Minimal technical overhead: There are no customer-managed ETL pipelines or scripts to maintain; the complexity of integration and transformation is abstracted away behind the platform.

Even for more complex use cases, such as the Cash Management module that needs direct banking integrations, typical implementations are described as taking two to three weeks rather than months. The upshot for CFOs and FP&A leaders is the possibility of achieving a single source of truth, and the new AI capabilities on top of it, without adding substantial technical debt or drawing heavily on scarce engineering resources.

From version control to a FinanceOS for the AI era

Datarails’ current positioning as a kind of “FinanceOS” for small and mid-sized businesses is the result of a strategic pivot rather than its original plan. When the company was founded in 2015 by Didi Gurfinkel (CEO), Eyal Cohen (COO), and Oded Har-Tal (CTO), its focus was on a narrower problem: enterprise spreadsheet version control.

In that early phase, the goal was to synchronize and manage Excel files across organizations. Adoption, however, was limited; the team struggled to find the right product-market fit around a purely version-control-centric offering. The turning point came in 2020, when the company realized its core insight was less about replacing Excel and more about augmenting it for finance teams.

By shifting toward an “Excel-native” automation philosophy and targeting SMB finance departments directly, Datarails repositioned itself around solving manual consolidation and data fragmentation. This pivot coincided with a period of rapid growth backed by significant venture funding: a $55 million Series A in June 2021 led by Zeev Ventures, followed by a $50 million Series B in March 2022 led by Qumra Capital.

The company was not immune to the broader tech downturn; in late 2022 it reduced its workforce by 18%. But by 2025, Datarails reported that it had nearly doubled its headcount to more than 400 employees worldwide, supported by a broader product portfolio that now includes Month-End Close and Cash Management modules in addition to its core planning and reporting capabilities.

That multi-product expansion sets the stage for the new AI agents. Rather than being freestanding copilots, they are layered on top of the operational “plumbing” Datarails has been building over several years.

Series C funding and the infrastructure behind the AI

The launch of the Strategy, Planning, and Reporting AI Finance Agents coincides with Datarails’ $70 million Series C round, led by growth equity firm One Peak with participation from existing investors including Vertex Growth and Vintage Investment Partners. The investment follows a year in which Datarails reports 70% revenue growth, driven in large part by new products.

According to the company, more than half of its growth in 2025 came from solutions launched in the preceding 12 months, notably:

• Datarails Month-End Close: A module focused on automating reconciliations and managing close workflows.
• Datarails Cash Management: A tool providing real-time visibility into cash and liquidity positions.

These applications form much of the operational backbone for the AI agents. By automating close processes and unifying cash data, they help ensure that when a CFO asks the AI a strategic or diagnostic question, the underlying numbers are timely and coherent. For finance leaders, this reinforces the idea that AI-driven insights are only as reliable as the transactional and consolidation layers beneath them.

From a strategic perspective, Datarails is channeling its new capital into deepening this stack: keeping Excel as the primary interface, strengthening the unified data layer, and expanding the reach and sophistication of its AI agents. The goal, in Gurfinkel’s words, is for the finance office to become “AI-native” without requiring users to abandon their preferred tools.

What this means for CFOs and FP&A leaders

Datarails’ bet is that the future of finance work will feel less like clicking through yet another dashboard and more like having a conversation with your company’s data. For CFOs, FP&A leaders, and finance-focused tech decision-makers, several practical implications emerge from the company’s current approach and product positioning:

• Faster narrative cycles: If the AI agents can reliably produce first-draft presentations and analyses, finance teams may spend less time building deliverables and more time refining insights and recommendations.
• Preservation of existing workflows: By keeping Excel at the center—as both a familiar interface and the format in which outputs can be audited—adoption friction may be lower than with platforms that require wholesale process changes.
• Governed AI usage: The use of Azure OpenAI and a unified internal data layer is intended to address key concerns around data leakage and hallucinations. While no vendor can fully eliminate risk, the architecture is clearly built with finance-sensitive constraints in mind.
• Lower IT burden: The “anti-implementation” promise, if it holds in practice, means finance leaders may be able to move forward on AI-augmented planning and reporting without large engineering projects or significant technical debt.

At the same time, there are open questions that any finance leader will want to evaluate directly: how the agents behave at the edge cases of complex consolidations, how well they handle unique accounting policies or industry-specific metrics, and how governance and change control are managed as prompts begin to drive more of the workflow.

Still, the direction of travel is clear. With its new AI Finance Agents, Datarails is arguing that CFOs do not need another dashboard so much as a conversational layer on top of their existing data and tools—one that can translate a prompt into a scenario analysis, a budget, or a board narrative, and then hand the results back in a format the finance team trusts.

For finance leaders exploring what “AI-native” might look like in their own organizations, Datarails’ combination of Excel-first design, unified data plumbing, and generative AI agents offers one concrete model of how that transition could unfold.