For the trendy CFO, the toughest a part of the job typically isn't the mathematics—it's the storytelling. After the books are closed and the variances calculated, finance groups spend days, generally weeks, manually copy-pasting charts into PowerPoint slides to clarify why the numbers moved.
At the moment, 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 approach 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 totally formatted property, not simply textual content.
A finance skilled can now ask, "What’s driving our profitability adjustments this yr?" or "Why did Advertising and marketing go over price range final month?" and the system will immediately generate board-ready PowerPoint slides, PDF reviews, 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 drawback 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 significant 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 prospects, they don't prefer to share the info in [an] open LLM," Gurfinkel famous. This enables the platform to make the most of state-of-the-art fashions whereas protecting information inside a safe enterprise perimeter.
Datarails’ new brokers sit on high of a unified information layer that connects these disparate methods. As a result of the AI is grounded within the firm’s personal unified inner information, it avoids the hallucinations widespread in generic LLMs whereas providing a degree of privateness required for delicate monetary information.
"If the CFO desires to leverage AI on the CFO degree 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 drawback first, Datarails can now provide brokers that perceive the context of the enterprise.
"Now the CFO can use our brokers to run evaluation, get insights, create reviews… 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 growth the place pure language prompts substitute advanced coding or guide 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 workforce themselves will be capable of develop purposes," Gurfinkel predicted. "The LLMs turn into so robust that in a single immediate, they’ll substitute full product runs."
He described a workflow the place a person might merely immediate: "That was my price range and my precise of the previous yr. Now construct me the price range for the following yr."
The brand new brokers are designed to deal with precisely a lot of these advanced, multi-variable eventualities. For instance, a person might ask, "What occurs if income grows slower subsequent quarter?" and obtain a state of affairs 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 typically 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 information migration, schema redesigns, and the inevitable friction of forcing non-technical customers to desert their most popular workflows. Datarails has engineered its method round this friction by constructing what may be greatest described as an "anti-implementation."
As an alternative of demanding a "rip and substitute" of legacy methods, the platform accepts the messy actuality of the trendy finance stack. The structure is designed to decouple the info storage from the presentation layer, successfully treating the group's present 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 just a few hours to perhaps just a few days".
From a technical perspective, this implies the "engineering" requirement is sort of solely stripped away. There aren’t any ETL pipelines to construct or Python scripts to keep up. 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 Basic Ledger to their Excel fashions in a self-service workflow. For modules like Month-Finish Shut, the corporate explicitly guarantees that "no IT assist is required," a phrase that seemingly comes as a reduction to stretched CTOs. Even advanced setups, akin to the brand new Money Administration module which requires banking integrations, are usually totally 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 workforce 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 drawback: model management for Excel. The preliminary premise was to synchronize and handle spreadsheets throughout enterprises, however adoption was sluggish because the workforce struggled to seek out the precise product-market match.
The breakthrough got here in 2020 with a strategic pivot. The workforce realized that finance professionals didn't need to substitute Excel with a brand new dashboard; they needed to repair Excel's limitations—particularly guide 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 fast 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 in the course of 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 staff globally, pushed by a multi-product enlargement technique that now contains Month-Finish Shut and Money Administration options.
Fueling the enlargement
The brand new AI capabilities are supported by the $70 million Collection C injection from One Peak, together with present buyers Vertex Progress, Classic Funding Companions, and others. The funding arrives after a yr of 70% income development for Datarails, pushed largely by the enlargement of its product suite.
Greater than 50% of the corporate's development in 2025 got here from options launched within the final 12 months, together with Datarails Month-Finish Shut (a instrument 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 purpose is to make the finance workplace "AI-native" with out forcing customers to desert their favourite instrument: 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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