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Stacks Secures £17M in Series A Funding to Revolutionize Enterprise Finance with AI

Stacks raises $23M in Series A funding to transform enterprise finance by integrating AI agents that streamline fragmented financial data.

Stacks, an innovative platform focused on enterprise finance, has successfully raised $23 million (£17 million) in a Series A funding round. This latest investment comes just a year after the company emerged from stealth mode with a seed round of $12 million (£8.9 million). The fintech firm aims to address the challenges of fragmented financial data by integrating various finance systems to provide users with a unified view of their financial landscape.

Founded by Albert Malikov, Stacks has developed a platform that not only consolidates disparate data but also incorporates AI agents that can automate operational workflows. “We started with the most manual and foundational workflows in finance: accounting and the close,” Malikov remarked. He emphasized that from the outset, the company has concentrated on resolving the fundamental issue of fragmented data. “By building an AI-ready data layer, we’re unlocking what’s needed to bring AI agents into operational finance, shifting CFO teams from process execution to higher-value analysis and decision-making,” he added.

The Series A round was led by Lightspeed Ventures and included participation from EQT Ventures, General Catalyst, and S16VC. Alex Schmitt, a partner at Lightspeed, expressed confidence in Stacks’ potential to address significant challenges in enterprise finance. “Stacks is uniquely positioned to tackle some of the toughest challenges in enterprise finance,” Schmitt stated, noting the technical and financial expertise that the team brings from previous roles at companies like Uber and Plaid.

The rise of fintech solutions like Stacks reflects a broader trend in the industry, where automation and data-driven decision-making are becoming increasingly critical. As organizations grapple with vast amounts of financial data, the ability to streamline processes and gain actionable insights is essential. Stacks’ approach aims to ease the burden on finance teams, allowing them to focus on strategic decision-making rather than just operational tasks.

The advent of AI in finance is poised to revolutionize the industry, with companies increasingly seeking technologies that enhance efficiency and accuracy. Stacks’ AI agents are designed to tackle routine tasks, thereby freeing up human resources for more complex analytical functions. This shift is particularly significant in the finance sector, where the demand for real-time data analysis and strategic foresight has never been higher.

With its recent funding, Stacks is well-positioned to expand its offerings and enhance its technology platform. As the company develops its AI capabilities further, it is likely to attract more clients looking for robust solutions to their finance operations. The emphasis on creating an AI-ready data layer could set a new standard for how financial data is managed and utilized across various sectors.

As Stacks continues to evolve, the broader implications of its technology could lead to a transformative impact on the finance industry. Companies that can successfully integrate AI and data analytics into their operations may find themselves at a distinct advantage in a rapidly changing economic landscape. Stacks represents a significant step forward in this shift, aiming to redefine the role of finance teams and how they leverage technology for better outcomes.

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Marcus Chen
Written By

At AIPressa, my work focuses on analyzing how artificial intelligence is redefining business strategies and traditional business models. I've covered everything from AI adoption in Fortune 500 companies to disruptive startups that are changing the rules of the game. My approach: understanding the real impact of AI on profitability, operational efficiency, and competitive advantage, beyond corporate hype. When I'm not writing about digital transformation, I'm probably analyzing financial reports or studying AI implementation cases that truly moved the needle in business.

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