Stacks raises $23m Series A to scale agentic AI

February 20, 2026

Stacks, an agentic AI platform for enterprise finance teams, has raised $23m in a Series A round.

The Series A was led by Lightspeed, with EQT Ventures, General Catalyst and S16VC doubling down on their investments.

The raise comes less than a year after the company secured $12m in seed funding led by General Catalyst. Since that seed round in February 2025, Stacks has onboarded 30 enterprise customers, including Epidemic Sound, Pleo, Cleo and Bloom & Wild.

Stacks claims to have saved finance teams more than 100,000 hours annually by automating reconciliations, journal entries and the month-end close.

Stacks has focused on addressing fragmented financial data, which is often spread across ERPs, spreadsheets, data lakes and legacy systems. The company has built a dedicated financial data layer that connects directly into finance systems to create a consistent view across platforms. It has also developed deterministic machine-learning tooling to ensure reliable automation at enterprise scale, before deploying AI agents to handle operational workflows across the finance stack.

Alex Schmitt, Partner at Lightspeed, said, “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.”

Alongside the funding, Stacks has launched AI Flux Analysis, a reporting and analysis suite designed to automate variance analysis and replace spreadsheet-based commentary with explainable, account-level investigation. The tool identifies variance drivers, pulls historical context and generates explanations for review, with early users reporting that reporting cycles have shifted from days to minutes, it claims.

Albert Malikov, Founder and CEO of Stacks, said, “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.”