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For the fashionable CFO, the toughest a part of the job usually isn't the maths—it's the storytelling. After the books are closed and the variances calculated, finance groups spend days, typically weeks, manually copy-pasting charts into PowerPoint slides to clarify why the numbers moved.
At this time, 11-year-old Israeli fintech firm Datarails introduced a set of recent generative AI instruments designed to automate that "final mile" of monetary reporting, successfully permitting finance leaders to "vibe code" their option to a board deck.
Launching at present to accompany the agency's newly introduced $70 million Collection C funding spherical, the corporate’s new Technique, Planning, and Reporting AI Finance Brokers promise to reply advanced monetary questions with absolutely formatted property, not simply textual content.
A finance skilled can now ask, "What’s driving our profitability modifications this 12 months?" or "Why did Advertising go over finances final month?" and the system will immediately generate board-ready PowerPoint slides, PDF stories, or Excel information containing the reply.
The deployment of those brokers marks a elementary shift in how the "Workplace of the CFO" interacts with information.
Past the chatbot
The promise of the brand new brokers is to unravel the fragmentation downside that plagues finance departments. Not like a gross sales chief who lives in Salesforce, or a CIO who depends on ServiceNow, the CFO has no single "system of fact". Information is scattered throughout ERPs, HRIS, CRMs, and financial institution portals.
A serious barrier to AI adoption in finance has been safety. CFOs are rightfully hesitant to plug P&L information into public fashions.
Datarails has addressed this by leveraging Microsoft’s Azure OpenAI Service. "We use the OpenAI in Azure to make sure the privateness and the safety for our clients, they don't prefer to share the info in [an] open LLM," Gurfinkel famous. This permits the platform to make the most of state-of-the-art fashions whereas conserving information inside a safe enterprise perimeter.
Datarails’ new brokers sit on prime of a unified information layer that connects these disparate methods. As a result of the AI is grounded within the firm’s personal unified inside information, it avoids the hallucinations widespread in generic LLMs whereas providing a stage of privateness required for delicate monetary information.
"If the CFO needs to leverage AI on the CFO stage or the group information, they should consolidate the info," defined Datarails CEO and co-founder Didi Gurfinkel in an interview with VentureBeat.
By fixing that consolidation downside first, Datarails can now supply brokers that perceive the context of the enterprise.
"Now the CFO can use our brokers to run evaluation, get insights, create stories… as a result of now the info is prepared," Gurfinkel stated.
'Vibe coding' for finance
The launch faucets right into a broader development in software program improvement the place pure language prompts substitute advanced coding or handbook configuration—an idea tech circles discuss with as "vibe coding." Gurfinkel believes that is the way forward for monetary engineering.
"Very quickly, the CFO and the monetary staff themselves will have the ability to develop functions," Gurfinkel predicted. "The LLMs turn out to be so sturdy that in a single immediate, they’ll substitute full product runs."
He described a workflow the place a consumer might merely immediate: "That was my finances and my precise of the previous 12 months. Now construct me the finances for the subsequent 12 months."
The brand new brokers are designed to deal with precisely these kind of advanced, multi-variable situations. For instance, a consumer might ask, "What occurs if income grows slower subsequent quarter?" and obtain a situation evaluation in return.
As a result of the output could be delivered as an Excel file, finance groups can confirm the formulation and assumptions, sustaining the audit path that generic AI instruments usually lack.
Ease of adoption: The 'anti-implementation'
For many engineering groups, the arrival of a brand new enterprise monetary platform alerts a looming headache: months of knowledge migration, schema redesigns, and the inevitable friction of forcing non-technical customers to desert their most well-liked workflows. Datarails has engineered its approach round this friction by constructing what could be greatest described as an "anti-implementation."
As a substitute of demanding a "rip and substitute" of legacy methods, the platform accepts the messy actuality of the fashionable finance stack. The structure is designed to decouple the info storage from the presentation layer, successfully treating the group's current Excel information as a frontend interface whereas Datarails acts because the backend database.
"We’re not changing something," Gurfinkel defined. "The implementation could be very quick, from a couple of hours to perhaps a couple of days".
From a technical perspective, this implies the "engineering" requirement is sort of totally stripped away. There are not any ETL pipelines to construct or Python scripts to take care of. The system comes pre-wired with over 200 native connectors—linking on to ERPs like NetSuite and Sage, CRMs like Salesforce, and varied HRIS and financial institution portals.
The heavy lifting is changed by a "no-code" mapping course of. A finance analyst, not a developer, maps the fields from their Normal Ledger to their Excel fashions in a self-service workflow. For modules like Month-Finish Shut, the corporate explicitly guarantees that "no IT help is required," a phrase that possible comes as a aid to stretched CTOs. Even advanced setups, corresponding to the brand new Money Administration module which requires banking integrations, are sometimes absolutely operational inside two to 3 weeks.
The result’s a system the place the "technical debt" often related to monetary transformation is rendered out of date. The finance staff will get their "single supply of fact" with out ever asking engineering to provision a database.
From model Management to imaginative and prescient management: a pivot that paid off
Datarails wasn't at all times the "FinanceOS" for the AI period. Based 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 downside: model management for Excel. The preliminary premise was to synchronize and handle spreadsheets throughout enterprises, however adoption was sluggish because the staff struggled to seek out the proper product-market match.
The breakthrough got here in 2020 with a strategic pivot. The staff realized that finance professionals didn't need to substitute Excel with a brand new dashboard; they needed to repair Excel's limitations—particularly handbook consolidation and information fragmentation. By shifting focus to SMB finance groups and embracing an "Excel-native" automation philosophy, the corporate discovered its stride.
This alignment led to speedy scaling, fueled by a $55 million Collection A in June 2021 led by Zeev Ventures, adopted shortly by a $50 million Collection B in March 2022 led by Qumra Capital. Whereas the corporate confronted headwinds through the tech downturn—leading to an 18% workforce discount in late 2022—it has since rebounded aggressively. By 2025, Datarails had almost doubled its workforce to over 400 workers globally, pushed by a multi-product growth technique that now consists of Month-Finish Shut and Money Administration options.
Fueling the growth
The brand new AI capabilities are supported by the $70 million Collection C injection from One Peak, together with current traders Vertex Progress, Classic Funding Companions, and others. The funding arrives after a 12 months of 70% income progress for Datarails, pushed largely by the growth of its product suite.
Greater than 50% of the corporate's progress in 2025 got here from options launched within the final 12 months, together with Datarails Month-Finish Shut (a device for automating reconciliations and workflow administration) and Datarails Money Administration (for real-time liquidity monitoring).
These merchandise function the "plumbing" that makes the brand new AI brokers efficient. By automating the month-end shut and unifying money information, Datarails ensures that when a CFO asks the AI a query, the underlying numbers are correct and up-to-date.
For Gurfinkel, the objective is to make the finance workplace "AI-native" with out forcing customers to desert their favourite device: Excel.
"We’re not changing something," Gurfinkel stated. "We join the Excel so Excel now turns into the calculation and the presentation."
With the launch of those new brokers, Datarails is betting that the way forward for finance isn't about studying new software program, however about having a dialog with the info you have already got.
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