For the fashionable CFO, the hardest a part of the job typically is not the math—it is 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 present, 11-year-old Israeli fintech firm Datarails introduced a set of latest generative AI instruments designed to automate that “final mile” of economic reporting, successfully permitting finance leaders to “vibe code” their approach to a board deck.
Launching immediately to accompany the agency’s newly introduced $70 million Collection C funding spherical, the company’s new Strategy, Planning, and Reporting AI Finance Agents promise to reply complicated monetary questions with totally formatted belongings, not simply textual content.
A finance skilled can now ask, “What’s driving our profitability modifications this yr?” or “Why did Advertising and marketing go over funds final month?” and the system will immediately generate board-ready PowerPoint slides, PDF reviews, or Excel recordsdata containing the reply.
The deployment of those brokers marks a basic shift in how the “Workplace of the CFO” interacts with knowledge.
Past the chatbot
The promise of the new brokers is to clear up 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 significant 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 guarantee the privateness and the safety for our prospects, they do not like to share the knowledge in [an] open LLM,” Gurfinkel famous. This permits the platform to make the most of state-of-the-art fashions whereas conserving 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 in the firm’s personal unified inner knowledge, it avoids the hallucinations widespread 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 want to consolidate the knowledge,” 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 reviews… as a result of now the knowledge is prepared,” Gurfinkel stated.
‘Vibe coding’ for finance
The launch faucets right into a broader pattern in software program growth the place pure language prompts exchange complicated coding or guide configuration—an idea tech circles refer to as “vibe coding.” Gurfinkel believes this is the future of economic engineering.
“Very quickly, the CFO and the monetary staff themselves can be ready to develop functions,” Gurfinkel predicted. “The LLMs change into so sturdy that in a single immediate, they’ll exchange full product runs.”
He described a workflow the place a consumer may merely immediate: “That was my funds and my precise of the previous yr. Now construct me the funds for the subsequent yr.”
The brand new brokers are designed to deal with precisely these kind of complicated, multi-variable eventualities. For instance, a consumer may 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 verify 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 indicators a looming headache: months of knowledge migration, schema redesigns, and the inevitable friction of forcing non-technical customers to abandon their most popular workflows. Datarails has engineered its approach round this friction by constructing what is likely to be greatest described as an “anti-implementation.”
As a substitute of demanding a “rip and exchange” of legacy techniques, the platform accepts the messy actuality of the fashionable finance stack. The structure is designed to decouple the knowledge storage from the presentation layer, successfully treating the group’s current Excel recordsdata as a frontend interface whereas Datarails acts as the backend database.
“We are not changing something,” Gurfinkel defined. “The implementation could be very quick, from a couple of hours to possibly a couple of days”.
From a technical perspective, this implies the “engineering” requirement is nearly totally stripped away. There are no ETL pipelines to construct or Python scripts to keep. The system comes pre-wired with over 200 native connectors—linking instantly 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 firm explicitly guarantees that “no IT assist is wanted,” a phrase that probably comes as a aid to stretched CTOs. Even complicated setups, akin to the new Money Administration module which requires banking integrations, are sometimes totally operational inside two to three weeks.
The consequence is 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 all the time 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 as the staff struggled to discover the proper product-market match.
The breakthrough got here in 2020 with a strategic pivot. The staff realized that finance professionals did not need to exchange Excel with a brand new dashboard; they needed 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 firm 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 firm confronted headwinds throughout the tech downturn—leading to an 18% workforce discount in late 2022—it has since rebounded aggressively. By 2025, Datarails had practically doubled its workforce to over 400 workers globally, pushed by a multi-product enlargement technique that now consists of 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 current 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 firm’s development in 2025 got here from options launched in 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 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 objective is to make the finance workplace “AI-native” with out forcing customers to abandon their favourite software: Excel.
“We are 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 is not about studying new software program, however about having a dialog with the knowledge you have already got.
Disclaimer: This article is sourced from external platforms. OverBeta has not independently verified the information. Readers are advised to verify details before relying on them.
