Model ML, the global AI workflow automation platform for financial services, has raised $75m in a Series A round led by FT Partners.
Significant participation also came from Y Combinator, QED, 13Books, Latitude and LocalGlobe. The raise comes just six months after the firm’s seed round and only twelve months after launch.
Use of proceeds from the round will focus on accelerating global expansion and deepening the company’s AI capabilities across key financial hubs. Model ML plans to build onboarding and customer success teams across San Francisco, New York, London and Hong Kong to support rising enterprise adoption.
The firm will also scale its AI engineering and infrastructure teams in New York and London, advancing its proprietary agentic systems and workflow automation modules. These investments aim to support seamless integration and deployment for its growing global client base.
Model ML, founded by brothers and repeat entrepreneurs Chaz and Arnie Englander, enables financial teams to build AI workflows that automatically generate client-ready Word, PowerPoint and Excel outputs directly from trusted data in exact prior formats. The platform is already deployed across several of the world’s largest banks, asset managers and consultancies, including two of the Big Four accounting firms.
The company’s agent workflows go beyond simple data retrieval or chat interfaces. They interpret schemas, reason across multiple sources, write code to extract and transform data, and generate full branded outputs such as long PowerPoint decks, diligence reports and investment memos.
Steve McLaughlin, founder and CEO of FT Partners, said, “Model ML is setting a new standard for how financial institutions leverage AI to achieve superior client results. While we expect significant efficiency gains, the true power of Model ML lies in the insights it will unlock for our clients, investors, and the broader FinTech ecosystem. We believe Model ML will fuel the next evolution of world-class service for our clients and transparency across all stakeholders in transactions.”
Chaz Englander, CEO of Model ML, said, “High-stakes business runs on documents; pitch decks, diligence summaries, investment memos. But most firms still build them the hard way.
“Analysts spend entire weekends cross-checking numbers and formatting slides. Despite all that effort, mistakes still slip through because no one can realistically verify every data point in a 100-page deliverable. That’s why we built Model ML. Our agents reason across data sources, write the code to extract and transform what’s needed, and generate finished, branded outputs with verification built in. We’re eliminating the grunt work so teams can focus on the analysis that actually matters.”
