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Model ML
Model ML builds AI agents embedded inside Excel, PowerPoint and deal systems, backed by $100M+ and advised by former CEOs of HSBC and UBS.
Model ML
Model ML was spearheaded by brothers Chaz and Arnie Englander, who hold the rare distinction of being backed by Y Combinator across three separate ventures. The firm sells AI-powered software that acts as a financial-services teammate, surfacing inside Microsoft Excel, PowerPoint, Outlook, a proprietary app, or a client’s existing systems. Its product targets the repetitive composition work that consumes junior bankers and analysts — slide polish, diligence summaries, deal-sourcing grids — turning multi-hour tasks into a few prompts. The platform spans investment banking, consulting, private equity and credit, and asset management. It connects directly to a firm’s internal and external data sources to generate compound intelligence, document review, and notetaking outputs that echo the house style. Clients include undisclosed “world’s leading financial institutions,” and the company operates from hubs in New York, San Francisco, London, and Hong Kong. Its architecture is deliberately channel-agnostic: the tool meets a user in whatever application they already have open, rather than pulling them into a separate environment. Model ML has raised in excess of $100 million from investors that include Y Combinator and a roster of heavyweight individual backers. The advisory board is composed exclusively of former financial-services chiefs: Sir Noel Quinn (ex-HSBC Group CEO), Axel Weber (ex-UBS Chairman), Mark Machin (ex-CPP Investments CEO), Philipp Rickenbacher (ex-Julius Baer CEO), Jeff McDermott (ex-UBS and Nomura Global Co-Head of Investment Banking), and Saul Nathan (ex-Morgan Stanley Capital Markets Chairman), among others. The firm is actively hiring across its four cities and draws its product and go-to-market talent directly from the institutions it sells to. Model ML’s structural differentiator is a forward-deployment model that embeds software within the live operating environment of a deal team, rather than forcing extraction to a standalone platform. This "comes to the deal, not the other way around" posture minimizes switching cost and lets the AI inherit the firm’s existing permission structures and data rooms. Coupled with an advisory board that doubles as a distribution map into the C-suites of global banks and asset managers, the company operates less as a pure-play SaaS vendor and more as an operating-system layer for institutional finance workflows.
General information
Firm type
other
Year founded
—
AUM
Undisclosed
Location
Region
North America
Country
United States
City
New York
Corporate office
West 38th St, New York, NY, United States
Additional offices
San Francisco, CA, United States · London, United Kingdom · Hong Kong
Principals
Chaz Englander
Co-Founder
Arnie Englander
Co-Founder
Sector focus
Frequently asked questions
Who founded Model ML, and what is their track record?
Model ML was founded by brothers Chaz and Arnie Englander. They are the only founding team to have been accepted into Y Combinator three times, an unusual signal of repeat-founder trust among early-stage investors. Prior ventures have not been named publicly, but the three-time YC backing gives the current entity a pre-existing relationship with a dense network of technology founders and allocators.
How does Model ML integrate with existing financial-services workflows?
The product is designed to live natively inside Microsoft Excel, PowerPoint, and Outlook, as well as a standalone Model ML App and a client’s proprietary systems. The architecture pulls in a firm’s internal and external data sources so that outputs — diligence memos, pitchbook pages, deal-sourcing grids — are generated in the same format the institution already uses, without requiring users to leave their primary tools.
What financial-services verticals does Model ML serve?
The company publicly lists investment banking, consulting, private equity and credit, and asset management as its customer verticals. Product modules — Workflows, Grids, Chat, Document Review, and Notetaker — are positioned to support tasks that run across all four, though the go-to-market emphasis skews toward the repetitive documentation and analysis work common in advisory and deal-execution roles.
What is the significance of Model ML's advisory board?
The advisory board is a roster of former global financial-institution CEOs and senior leaders, including Sir Noel Quinn (HSBC), Axel Weber (UBS), Mark Machin (CPP Investments), Philipp Rickenbacher (Julius Baer), and Jeff McDermott (UBS/Nomura). This group provides both product-requirements credibility for an AI tool sold into regulated banks and a direct relationship map into the C-suites of target customers — functioning as an embedded distribution channel.
How much capital has Model ML raised, and from whom?
Model ML has raised north of $100 million in total investment. Confirmed backers include Y Combinator, though the firm does not disclose a full cap-table roster. The $100-million figure and the YC relationship are stated directly on the company’s own About page, positioning it as a well-capitalized entrant into institutional financial-technology.
Where does Model ML operate geographically?
The firm lists four office locations: West 38th Street in New York, Market Street in San Francisco, King’s Cross in London, and Stanley Street Central in Hong Kong. This footprint gives it physical proximity to both North American and European banking hubs and to Asia-Pacific wealth and asset-management centers.
How does Model ML handle data security and privacy?
The firm markets an institution-grade security environment built for the strictest data-privacy rules, stating that customer data stays the customer’s and is handled securely across every connected system. It does not publish a technically detailed security whitepaper publicly, but its sales positioning is aimed at regulated financial institutions that impose high bars on data residency and auditability.
Profile maintained by Altss using OSINT (open-source intelligence), regulatory filings, licensed data partners, and verified direct submissions. Read the methodology. Last updated: . Continuous refresh with full update cycles at least every 30 days.
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