Investment bankers and financial analysts are notorious for pulling all-nighters to double-check pitch decks and investment memos. It is a grueling rite of passage that often leads to burnout and costly errors in high-stakes deals. London and New York-based Model ML has officially raised $75 million to fix this broken workflow forever. This massive Series A funding round signals a major shift in how Wall Street plans to handle its most tedious tasks using advanced artificial intelligence.
A Rapid Ascent Powered by Top Tier Investors
The speed at which Model ML has secured capital is turning heads across the venture capital landscape. Just six months after closing their seed round, the company has successfully banked another $75 million. This Series A round was led by FT Partners, a firm renowned for its deep expertise in financial technology.
It is rare for a startup to command this level of capital so soon after launching. The participation list reads like a who’s who of the investment world. Alongside FT Partners, the round saw backing from Y Combinator, QED Investors, 13Books, Latitude, and LocalGlobe.
The involvement of Y Combinator and LocalGlobe suggests strong confidence in the foundational technology built by founders Chaz and Arnie Englander. These investors are betting that Model ML is not just another wrapper for generic AI models but a specialized tool capable of navigating the complex regulatory environment of global finance.
- Lead Investor: FT Partners
- Key Participants: Y Combinator, QED, 13Books, Latitude, LocalGlobe
- Timeline: Series A closed 6 months after Seed; 12 months after launch
- Founders: Chaz and Arnie Englander
Steve McLaughlin, founder of FT Partners, has often emphasized the need for purpose-built technology in banking. By leading this round, his firm is putting significant money behind the belief that Model ML is the solution the industry has been waiting for.
Model ML founders Chaz and Arnie Englander financial AI automation
replacing The Grunt Work With Agentic AI
The core promise of Model ML is simple yet transformative. It wants to eliminate the “grunt work” that consumes junior analysts’ lives. Financial services firms run on documents. They produce endless pitch decks, diligence summaries, and investment memos. Creating these documents currently requires manually extracting data, formatting slides, and cross-checking numbers across dozens of sources.
Model ML uses what is known as “agentic AI systems.” unlike a standard chatbot that just answers questions, these AI agents can perform multi-step tasks. They reason across data sources. They write code to extract information. They verify the data against trusted internal documents.
“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.”
This capability creates a bespoke “AI brain” for each organization. The platform connects strictly to a company’s trusted data sources. This ensures that the AI does not hallucinate facts or pull information from unreliable parts of the open internet. Security and accuracy are the primary currencies in financial services, and Model ML seems to have built its architecture with this in mind.
Adoption By The Big Four And Global Banks
The technology is already being battle-tested by some of the most demanding clients in the world. Model ML reports that its software is currently in use by several of the world’s largest banks and asset managers. Perhaps most significantly, two of the “Big Four” accounting firms have already adopted the platform.
Winning contracts with Big Four firms is notoriously difficult due to their stringent compliance and data privacy requirements. This early adoption serves as a powerful validation of the platform’s security and utility.
Comparison: Traditional Workflow vs. Model ML Workflow
| Feature | Traditional Analyst Workflow | Model ML AI Workflow |
|---|---|---|
| Data Gathering | Manual search through PDFs and spreadsheets | Automated extraction from trusted data lakes |
| Verification | Human “eyeball” check (prone to error) | Code-based verification of every data point |
| Time Required | Days or Weekends | Minutes or Hours |
| Focus | Formatting and Copy-Pasting | Strategic Analysis and Decision Making |
The company plans to use this fresh $75 million injection to fuel global expansion. With headquarters already established in London and New York, they are perfectly positioned to serve the two most important financial hubs on the planet. The funding will also go toward hiring engineering talent to further refine their AI agents.
A New Era For Financial Labor
This funding news arrives at a critical moment for the financial services industry. Banks are struggling to retain top talent who are increasingly disillusioned by the repetitive nature of the work. By automating the low-value tasks, Model ML is not just saving time but potentially saving the mental health of thousands of workers.
The founders, Chaz and Arnie Englander, are repeat entrepreneurs who understand this pain point intimately. Their vision is to let computers handle the data transformation so that humans can focus on the actual analysis.
When an analyst does not have to spend Saturday night formatting a logo on a slide, they can spend that time thinking about the strategic implications of a merger. This shift from production to strategy could fundamentally change the skill set required for future investment bankers.
We are seeing a trend where “Vertical AI”—artificial intelligence built for a specific industry—is outperforming general-purpose tools. Model ML is a prime example of this vertical approach. They are not trying to be everything to everyone. They are trying to be the absolute best tool for a banker building a pitch deck.
Key Takeaways from the Raise:
- The $75M amount is exceptionally high for a Series A in the current market.
- The tool is designed to integrate, not replace, existing workflows.
- Verification is built-in, addressing the biggest fear regarding AI in finance.
The financial sector is often slow to adapt to new technology. However, the pressure to reduce costs and increase speed is forcing even the oldest institutions to look at automation. With $75 million in the bank and heavy hitters like FT Partners and Y Combinator in their corner, Model ML is poised to become the standard operating system for deal-making.
Conclusion
Model ML has successfully secured $75 million to bring sanity back to the financial services industry. By automating the tedious creation of pitch decks and memos, they are offering a lifeline to overworked analysts and a competitive advantage to firms that adopt early. The backing of FT Partners and the adoption by Big Four firms proves that this is more than just hype. It is a necessary evolution of Wall Street. As AI continues to penetrate high-stakes industries, the question is no longer if workflows will be automated, but how quickly.
We want to hear from you. Are you working in finance and tired of the manual grunt work? Would you trust an AI to build your pitch deck? Share your thoughts in the comments below or tag us on social media using #ModelMLSeriesA.
