Model ML, an AI-powered automation platform for financial services, has raised $75 million in a Series A round to accelerate its global expansion. The London and New York based startup focuses on eliminating the repetitive document production work that underpins dealmaking and advisory processes. Its software generates fully formatted, client-ready materials directly from trusted data sources, aiming to reduce workloads for investment and advisory teams by double-digit percentages.
Funding Round and Investor Backing
The $75 million Series A was led by FT Partners, a specialist firm with deep ties to the global fintech and financial services ecosystem. Existing and new backers including Y Combinator, QED, 13Books, Latitude and LocalGlobe also participated, signaling strong conviction from both venture and sector-focused investors. The round comes only six months after Model ML’s seed raise led by LocalGlobe and roughly a year after the company’s launch, underscoring the pace at which demand for its technology has grown.
Founders and Entrepreneurial Track Record
Model ML was founded by brothers Chaz and Arnie Englander, repeat entrepreneurs who previously built and scaled companies such as Fat Llama and Fancy, both backed by Y Combinator. Their experience in launching and growing venture-backed businesses informs how they are approaching product development, enterprise sales and global scaling in a highly regulated industry. The Englanders created Model ML after seeing first-hand how much time financial professionals lose to manual tasks and fragmented workflows around documents.
Product and Technology
At its core, Model ML provides agentic AI workflows that can interpret complex financial data models, reason across multiple information sources and then write the code needed to extract, transform and aggregate that data. The platform produces fully branded pitch decks, diligence reports and investment memos that replicate a client’s existing formatting standards, while embedding verification steps to reduce the risk of errors. The system is described by the company as a bespoke AI brain for each organization, with every deployment tailored to a client’s data stack, processes and governance requirements.
Automating High Stakes Financial Workflows
Model ML is designed to integrate directly into existing workflows rather than replace them, pulling only from an institution’s own bank of trusted sources instead of scraping external or unverified data. By automating the heavy lifting of compiling numbers, cross-checking figures and building slide decks, the platform aims to free analysts and associates from repetitive work so they can focus on higher value analysis and client interaction. According to the company, early deployments have shown material reductions in workloads for investment banking and advisory teams, in some cases cutting effort by around 20 percent.
Performance and Competitive Positioning
In internal and client-run benchmarks, Model ML’s system has reportedly completed document preparation tasks in under three minutes that previously took more than an hour for leading consulting firms such as McKinsey and Bain to deliver. The company says its outputs have also matched or exceeded human teams on accuracy, thanks to its verification-focused architecture that checks figures against underlying data before documents are finalized. Unlike tools that center only on analytics or robotic process automation, Model ML positions itself at the intersection of deep financial workflow knowledge and specialized AI, explicitly built for banks, asset managers and consultancies.
Customer Base and Market Reach
Model ML states that its technology is already in use at several of the world’s largest banks, asset managers and consulting firms, including two of the Big Four accounting firms. These clients deploy the platform across use cases such as capital markets pitch books, M&A and financing diligence reports, portfolio monitoring packages and internal investment committee materials. As organizations face mounting pressure to operate with leaner teams while handling growing volumes of data, the company is betting that AI-native workflows will become embedded in how front office and advisory teams operate.
Growth Plans and Use of Capital
The newly raised capital will be allocated to expanding across key financial hubs, with New York, London and Hong Kong highlighted as immediate priorities. Model ML plans to significantly grow its AI engineering teams and strengthen its implementation and customer success functions to support large global rollouts. Additional investment will also go into advancing its proprietary agentic AI stack, with the goal of covering a wider range of financial workflows and maintaining strong controls around data security and compliance.
Model ML’s $75 million Series A marks a rapid scaling phase for a startup that has moved from launch to large enterprise adoption in roughly a year. By targeting the document-heavy work that underpins capital markets, asset management and advisory services, the company is positioning its platform as core infrastructure for financial institutions rather than a peripheral productivity tool. If it can continue to demonstrate faster execution and higher accuracy at scale, Model ML is likely to become a defining player in how AI reshapes high stakes financial workflows in the coming years.