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For the fashionable CFO, the toughest a part of the job usually 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 latest generative AI instruments designed to automate that "final mile" of monetary reporting, successfully permitting finance leaders to "vibe code" their technique to a board deck.
Launching at present to accompany the agency's newly introduced $70 million Sequence C funding spherical, the corporate’s new Technique, Planning, and Reporting AI Finance Brokers promise to reply complicated monetary questions with absolutely formatted belongings, not simply textual content.
A finance skilled can now ask, "What’s driving our profitability adjustments this 12 months?" or "Why did Advertising go over funds 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 knowledge.
Past the chatbot
The promise of the brand new brokers is to resolve the fragmentation drawback that plagues finance departments. In contrast to a gross sales chief who lives in Salesforce, or a CIO who depends on ServiceNow, the CFO has no single "system of reality". 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 knowledge 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 wish 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 holding knowledge inside a safe enterprise perimeter.
Datarails’ new brokers sit on prime of a unified knowledge layer that connects these disparate techniques. As a result of the AI is grounded within the firm’s personal unified inside knowledge, it avoids the hallucinations frequent in generic LLMs whereas providing a stage of privateness required for delicate monetary knowledge.
"If the CFO needs to leverage AI on the CFO stage or the group knowledge, 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 supply 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 pattern in software program improvement the place pure language prompts substitute complicated coding or guide configuration—an idea tech circles check with as "vibe coding." Gurfinkel believes that is the way forward for monetary engineering.
"Very quickly, the CFO and the monetary group themselves will be capable to develop purposes," Gurfinkel predicted. "The LLMs change into so robust that in a single immediate, they will substitute full product runs."
He described a workflow the place a consumer might merely immediate: "That was my funds and my precise of the previous 12 months. Now construct me the funds for the subsequent 12 months."
The brand new brokers are designed to deal with precisely these kind of complicated, multi-variable eventualities. For instance, a consumer 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 might 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 indicators 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 approach round this friction by constructing what is likely to be finest described as an "anti-implementation."
As a substitute of demanding a "rip and substitute" of legacy techniques, 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 aren’t changing something," Gurfinkel defined. "The implementation might be very quick, from a number of hours to perhaps a number of days".
From a technical perspective, this implies the "engineering" requirement is nearly 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 Basic 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 reduction to stretched CTOs. Even complicated setups, resembling 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" normally related to monetary transformation is rendered out of date. The finance group will get their "single supply of reality" 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 group struggled to seek out the fitting product-market match.
The breakthrough got here in 2020 with a strategic pivot. The group realized that finance professionals didn't wish to substitute Excel with a brand new dashboard; they wished to repair Excel's limitations—particularly guide consolidation and knowledge 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 Sequence A in June 2021 led by Zeev Ventures, adopted rapidly by a $50 million Sequence 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 workers globally, pushed by a multi-product growth technique that now contains Month-Finish Shut and Money Administration options.
Fueling the growth
The brand new AI capabilities are supported by the $70 million Sequence C injection from One Peak, together with current traders Vertex Development, 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 software 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 knowledge, 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 software: Excel.
"We aren’t 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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