AI fintech Model ML raises $75 million in a Series A funding round led by FT Partners
Model ML, a leading global AI workflow automation platform for financial services, today announced a $75 million Series A round led by FT Partners, the global leader in FinTech investment banking. The round included major participation from Y Combinator, QED, 13Books, Latitude, and LocalGlobe, and comes just six months after the company’s seed raise — also led by LocalGlobe — and only a year after its launch.
“We’re thrilled to partner with such an exceptional group of investors as we pursue our mission to transform how financial institutions operate,” said Chaz Englander, CEO of Model ML. “This financing enables us to accelerate our global expansion and advance our AI capabilities across key financial hubs as enterprise demand continues to surge. FT Partners is the ideal strategic partner — Steve McLaughlin and his team have long pioneered the use of data and technology in investment banking, and our close collaboration will demonstrate how AI can redefine the entire financial advisory workflow.”
“Model ML is establishing a new benchmark for how financial institutions apply AI to deliver superior client outcomes,” said Steve McLaughlin, Founder and CEO of FT Partners. “While the efficiency gains will be significant, the real impact lies in the insights Model ML will unlock for our clients, investors, and the broader FinTech ecosystem. We believe the company will power the next evolution of world-class client service and promote greater transparency across all stakeholders in a transaction.”
Founded by brothers and repeat entrepreneurs Chaz and Arnie Englander, Model ML enables financial teams to build AI-driven workflows that produce client-ready Word, PowerPoint, and Excel outputs directly from trusted data — preserving exact formats and branding. This capability scales across entire organizations and is already deployed at several of the world’s largest banks, asset managers, and consultancies, including two of the Big Four accounting firms.
High-stakes deliverables such as pitch decks, investment memos, and diligence reports are still created through slow, manual processes that strain teams and impede momentum. Deal teams at every seniority level spend countless hours formatting outputs and reconciling inconsistencies across documents — introducing operational risk and slowing decision-making.
Model ML closes this gap. Its agent workflows do far more than retrieve data or power chat interfaces — they interpret schemas, reason across multiple sources, write the code required to extract and transform data, and generate finished, branded deliverables such as long-form decks, research reports, and investment memos, all with built-in verification.
Verification is one of Model ML’s key differentiators. In a recent workflow test, the platform was benchmarked against consultants from McKinsey and Bain on real Word and PowerPoint deliverables. The consultants needed more than an hour; Model ML completed the task in under three minutes — and still caught more errors. In short, it was not just 20× faster, but more accurate.
Chaz added, “High-stakes business runs on documents — pitch decks, diligence summaries, investment memos. Yet most firms still produce them the hard way. Analysts spend entire weekends cross-checking numbers and formatting slides, and mistakes still slip through because no one can realistically verify every data point in a hundred-page deliverable. That’s why we built Model ML. Our agents reason across datasets, write the code to transform what’s needed, and generate polished, branded outputs with verification built in. We’re removing the grunt work so teams can focus on the analysis that truly matters.”
