Stacks, an agentic platform for enterprise finance, has raised £17 million in a Series A led by Lightspeed, with participation from EQT Ventures, General Catalyst, and S16VC. It connects finance systems to create a single financial view and deploys agents that automate workflows such as reconciliations, journal entries, and the month-end close.
Fragmented transaction data across ERPs, spreadsheets, data lakes, and legacy systems forces finance teams into manual workarounds and limits real-time visibility. Stacks built a data layer that connects directly to finance systems and creates a consistent financial view across them, alongside deterministic machine-learning tooling designed to make automation reliable at enterprise scale.
Since emerging from stealth less than a year ago, more than 30 enterprise customers globally have onboarded, saving finance teams over 100,000 hours annually by automating operational workflows. Customers include European scaleups such as Volt, Motorway, Cleo and Bloom & Wild.
Stacks also introduced AI Flux Analysis, a reporting and analysis product that automates variance analysis and replaces spreadsheet-based commentary with account-level investigation. Flux identifies variance drivers across transactions, pulls historical context across periods, and generates explanations that finance teams can review and refine.
Founded in Amsterdam and headquartered in London, Stacks targets both the $100 billion Office of the CFO software market and enterprise finance labour spend with an AI-native platform built from the ground up. With its data foundation in place, Stacks is expanding into financial intelligence.
Stacks is uniquely positioned to tackle some of the toughest challenges in enterprise finance. The team’s mix of technical and finance expertise from Uber and Plaid, along with the company’s remarkable traction, gives us strong conviction that they will lead the AI shift inside the Office of the CFO.
We started with the most manual and foundational workflows in finance: accounting and the close. From day one, we focused on solving the core problem: 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.
With Stacks, we’re saving a noticeable amount of time on the finance close, and as a result we can produce the management accounts faster each month.
