Datarails raises $70M to bring AI Finance Agents to CFOs struggling with fragmented data

Datarails Secures $70M to Transform How CFOs Work with Data

Datarails, an 11-year-old Israeli fintech company, announced a $70 million Series C funding round led by One Peak, with participation from Vertex Growth, Vintage Investment Partners, Zeev Ventures, Innovation Endeavors, ClalTech, and Qumra Capital1

1

2

2

. This brings the company's total funding to over $175 million as it positions itself to reshape the Office of the CFO with AI for finance2

2

. The funding arrives alongside the launch of new AI Finance Agents designed to tackle what CEO and co-founder Didi Gurfinkel describes as the most time-consuming challenge facing finance teams: transforming raw numbers into compelling narratives for stakeholders1

1

.

New AI Finance Agents Automate the Last Mile of Financial Reporting

The newly launched Strategy, Planning, and Reporting AI Finance Agents represent a departure from traditional chatbots by delivering fully formatted assets rather than just text responses. Finance professionals can now ask conversational questions like "What's driving our profitability changes this year?" or "Why did Marketing go over budget last month?" and receive board-ready PowerPoint slides, PDF reports, or Excel files containing the answer1

1

2

2

. These generative AI tools for finance eliminate the weeks finance teams typically spend manually copy-pasting charts into presentations after closing the books1

1

. The agents can also handle complex scenario analysis, such as "What happens if revenue grows slower next quarter?" while maintaining the audit trail that generic AI tools often lack1

1

.

Solving Data Fragmentation Across the Finance Stack

Unlike sales leaders who work primarily in Salesforce or CIOs who rely on ServiceNow, CFOs face a fundamental challenge: they have no single source of truth. Critical financial data sits scattered across ERPs, HRIS systems, CRMs, and bank portals1

1

. Datarails addresses this through its AI-native financial platform called FinanceOS, which consolidates data from these disparate systems while allowing finance teams to continue working in Excel-based finance environments they already know2

2

. The platform comes pre-wired with over 200 native connectors linking directly to ERPs like NetSuite and Sage, CRMs like Salesforce, and various HRIS and bank portals1

1

. According to the company, 99% of finance professionals currently spend around three hours daily buried in spreadsheets due to siloed workflows encompassing financial planning and analysis, month-end close, and spend control2

2

.

Security and Privacy Through Azure OpenAI Service

A major barrier to AI adoption in finance has been security concerns around sharing sensitive P&L data with 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 explained1

1

. Because the AI is grounded in the company's own unified internal data, it avoids the hallucinations common in generic LLMs while meeting the stringent privacy requirements for sensitive financial information1

1

. This architecture enables AI-driven insights without compromising the security posture CFOs demand2

2

.

Vibe Coding for Finance: The Future of CFO Tools

The launch taps into a broader trend where natural language prompts replace complex coding or manual configuration—a concept known as vibe coding for finance. Gurfinkel believes this represents the future of financial engineering: "Very soon, the CFO and the financial team themselves will be able to develop applications. The LLMs become so strong that in one prompt, they can replace full product runs"1

1

. He described workflows where users could simply prompt: "That was my budget and my actual of the past year. Now build me the budget for the next year"1

1

. This shift matters because it democratizes business intelligence capabilities that previously required data science teams or extensive technical knowledge.

Rapid Growth and Strategic Expansion Plans

Datarails grew its revenue by more than 70% in the last year while doubling its team to more than 400 employees globally in 20252

2

. The company plans to use the new funding to accelerate expansion across North America, Europe, the Middle East, and Africa, increase its research and development budget, and pursue strategic acquisitions in the coming months2

2

. One Peak co-founder and Managing Director David Klein noted that "its Excel-native approach is brilliant because it meets CFOs where they already work, while the multiproduct strategy demonstrates the kind of ambitious, category-defining vision we look for"2

2

. Implementation can be completed in as little as a few hours to a few days, with no ETL pipelines to build or Python scripts to maintain1

1

. This "anti-implementation" approach signals workflow automation that adapts to existing processes rather than forcing wholesale changes—a critical consideration as finance teams evaluate whether to invest in AI capabilities that could reshape how they deliver strategic analysis to leadership.