How does Model ML's AI workspace differ from traditional office suites like Word, PowerPoint, and Excel?

While Model ML offers similar functionalities, its core difference is that it's built on an agentic AI system. This means it can directly access and process information from a firm's internal files, emails, CRMs, and external data sources, significantly reducing the time spent on manual data manipulation.

What has been the impact of AI advancements on Model ML's product capabilities?

Recent AI advancements, particularly in vision models and function calling, have significantly improved Model ML's capabilities. The AI can now analyze information from tables and charts within documents, making it more accurate and efficient than human analysis for many data-gathering tasks.

Why are financial firms, historically hesitant to adopt new software, now embracing AI solutions like Model ML?

The past year has seen a significant shift, with financial firms moving from testing AI to actively using it. This is driven by AI being a top priority for CEOs and executives, who recognize the competitive advantage and tangible value these advanced AI solutions offer.

What are the key lessons Arie and Chaz learned from their previous startups, Fat Llama and Fancy?

From Fat Llama, they learned the importance of perseverance and belief in an idea, even when facing initial skepticism. Fancy taught them the value of rapid iteration, customer-centricity, and navigating intense operational challenges, especially during unexpected events like COVID-19.

What is the most important factor when building a startup team, according to the founders?

The founders emphasize that the most crucial factor is enjoying working with your co-founder and team members. Beyond skills and experience, cultural fit, transparency, honesty, and a shared passion for the work are paramount to overcoming the inevitable challenges of building a startup.

What advice do the founders give to young individuals considering a career in startups or AI?

They advise honesty about the immense difficulty and commitment required for building a startup. Passion and perseverance are key. If the intense journey sounds appealing, go all in. Otherwise, gaining experience elsewhere before diving in might be a better path. They stress the invaluable learning that comes from the startup experience, regardless of the outcome.

Should European founders base their companies in the US or Europe?

While acknowledging the unique buzz and network in the Bay Area, the founders suggest that ambitious European founders should consider building in tier-one cities in Europe like London. They note that while talent is competitive and expensive in the US, Europe offers strong, less competitive engineering talent pools. However, they also highlight the challenge of finding a hard-working culture in Europe compared to San Francisco.

The Finance Startup Bringing Agentic AI to Wall Street

Y Combinator

Science & Technology7 min read47 min video

Jul 31, 2025|58,351 views|918|29

YC Y Combinator

Save to Pod

Key Moments

On this page

TL;DR

Model ML provides an AI workspace for financial services, automating tasks for investment banks and PE firms. Founders leverage AI to analyze deals and create client-ready content.

Key Insights

Model ML offers an AI-powered workspace designed specifically for financial services, mirroring the functionality of Word, PowerPoint, and Excel.

The platform integrates with a firm's existing digital assets (files, emails, CRM, data vendors) to create a 'cognitive architecture' for analysis and automation.

Early adoption is strong, with 10% of the world's top investment banks and private equity firms already using the product, and contract signings increasing significantly.

The founders, experienced entrepreneurs who sold two previous YC companies, were motivated to build Model ML after experiencing the manual and repetitive nature of financial analysis.

Significant advancements in AI models, particularly in function calling and vision capabilities (OCR, table/chart reading), have accelerated the product's effectiveness and accuracy beyond human capabilities for certain tasks.

The financial services industry, previously slow to adopt software, is now actively seeking AI solutions, with Model ML securing deals at the CEO level.

Previous entrepreneurial experiences with 'Fancy' (grocery delivery) and 'Fat LLaMA' (item rental marketplace) have instilled lessons in perseverance, customer-centricity, and handling immense challenges.

The siblings' co-founding relationship is characterized by transparency, honesty, and a clear division of engineering/product and finance/commercial responsibilities, with shared passion for customer impact.

Building impactful products that people enjoy using, rather than focusing solely on making money, is the primary motivation for the founders, especially in the AI space where user experience is key.

For aspiring founders, the advice is to be prepared for the difficulty, be passionate, persevere, and consider starting a company early to learn rapidly and mitigate opportunity cost.

INTRODUCTION TO MODEL ML AND ITS AI WORKSPACE

Model ML is an AI-powered workspace tailored for the financial services industry, developed by brothers Arnie and Chaz Englander, who previously founded two successful Y Combinator companies. Their platform functions as an advanced suite, akin to Word, PowerPoint, and Excel, but built upon an 'agentic' system. This system replicates the digital access that human professionals have within firms—including files, emails, CRMs, data vendors, and public information—to automate complex tasks and streamline workflows.

TANGIBLE VALUE AND MARKET TRACTION

The company is experiencing significant growth, evidenced by signing as many contracts in the last seven days as in the entire previous quarter. This rapid adoption highlights the clear, tangible value Model ML delivers. Currently, approximately 10% of the world's top investment banks and private equity firms utilize their product. This success underscores a pivotal shift in the financial sector, where AI solutions are no longer being tested but actively implemented due to their productivity gains.

ORIGINS AND MOTIVATION FOR FOUNDERS

The idea for Model ML stemmed from the founders' personal experience after selling their previous companies. While engaging in their own investing, they found the process of deal analysis to be manual and repetitive. They built an initial agentic system to automate the creation of one-page summaries for incoming opportunities. This tool significantly augmented their analysis by pulling information from various sources beyond the initial email, such as LinkedIn, company databases, and review sites, revealing the power of automated information gathering.

AUTOMATING FINANCIAL ANALYSIS AND REPORTING

Model ML addresses the time-consuming tasks analysts and associates perform, such as creating quarterly earnings summaries from public filings. Traditionally, these processes take days, involving manual data collation and cross-referencing from multiple sources like company filings and data providers like FactSet. Model ML automates this by integrating data sources into an Excel-like interface, allowing for seamless export into presentation formats, often achieving higher accuracy and saving considerable time.

THE EVOLUTION OF AI AND MODEL ML'S CAPABILITIES

The progress in AI over the past year has been a critical enabler for Model ML. Improvements in function calling and, notably, vision models have revolutionized capabilities. The ability for AI to perform OCR and interpret data from tables and charts in documents has dramatically enhanced the analysis of files and filings. The founders emphasize that AI models are now often more accurate than humans for data gathering and presentation tasks, a significant shift from mere testing to deployment.

SHIFTING INDUSTRY PERCEPTION AND SALES STRATEGY

Financial institutions, historically hesitant to adopt new software, have become highly curious about AI. This year marks a transition from proof-of-concept pilots to actual contract signings. Model ML's sales strategy targets C-suite executives, recognizing that AI integration is a top-level priority, not a departmental concern. The emphasis is on demonstrating immediate, short-term value and positioning Model ML as a leading AI solution in financial services, making it imperative for senior leaders to engage.

GLOBAL OPERATIONS AND BUILDING TRUST

Model ML operates globally, with teams in London, New York, Hong Kong, and Singapore, reflecting the international nature of their client base. Building trust with these high-stakes clients is paramount, especially given the potential career implications of wrong technology adoption decisions. Demonstrations are conducted with real data and use cases, and significant effort is invested in building personal relationships ('FaceTime') and tailoring solutions to specific client needs to foster confidence.

LESSONS FROM PREVIOUS VENTURES: PERSEVERANCE AND CUSTOMER FOCUS

The founders' previous experiences with 'Fancy' (a grocery delivery service) and 'Fat LLaMA' (an item rental marketplace) provided invaluable lessons. They stress the importance of enjoying the entrepreneurial journey and being prepared for extreme challenges. Perseverance, when based on logical sense rather than blind optimism, is key. Both companies faced numerous setbacks, including payment processing issues and logistical hurdles, but ultimately succeeded through a relentless focus on the customer and iterating based on direct feedback.

HIRING PHILOSOPHY AND BUILDING A STRONG TEAM

A critical learning from past ventures is the importance of hiring. Model ML prioritizes cultural fit and passion for the work over just credentials. They emphasize rigorous hiring to ensure team members genuinely enjoy their roles and exhibit a strong work ethic, often by observing what individuals build in their personal time. This deliberate and slow hiring process aims to build a cohesive, motivated team capable of sustained effort, especially during the critical early stages of growth.

IMPACTFUL INNOVATION AND THE FUTURE OF FINANCE AI

The founders are driven by the impact their work has, finding it more motivating than financial gain alone. They see AI's potential not just to automate but to create entirely new possibilities that users might not even envision. The current focus is on rebuilding core financial tools (PowerPoint, Word, Excel) with AI at their core, anticipating that future advancements will lead to even more autonomous operations where user interfaces become less critical as tasks execute automatically.

CO-FOUNDER DYNAMICS: TRANSPARENCY AND SHARED VISION

As siblings and co-founders, Arnie and Chaz Englander benefit from a high degree of transparency and honesty, which they believe is crucial for preventing founder fallout—a common reason for startup failure. Their complementary skill sets, with one focusing on engineering/product and the other on finance/commercials, create a productive dynamic. Both share a deep interest in product and customer impact, ensuring alignment on the company's core mission and execution.

ADVICE FOR ASPIRING ENTREPRENEURS IN AI

For young individuals considering a career in AI or entrepreneurship, the advice is to be prepared for the immense difficulty and demands of building a startup. Passion and perseverance are essential. They advocate for starting companies early, as the learning curve is steep, and the opportunity cost of not taking risks early is higher. The worst-case scenario in founding a startup is learning immensely over a short period, a valuable outcome that opens doors for future endeavors.

BUILDING INVENTIVE COMPANIES: SAN FRANCISCO VS. EUROPE

While acknowledging the appeal and innovation hub of San Francisco, the founders, based in London, note the challenges in replicating Silicon Valley's intense work culture and entrepreneurial buzz in Europe. They find hiring individuals with extreme work ethics more common in SF. However, Europe offers strong engineering talent at potentially lower costs and less competition than the Bay Area. Ultimately, they advise founders to be as close to their customers and the core entrepreneurial community as possible, often favoring proximity to US markets.

Mentioned in This Episode

●Software & Apps

●Companies

●Organizations

●People Referenced

Common Questions

Model ML provides an AI workspace designed for financial services. It tackles the problem of manual and repetitive tasks by creating an integrated system that acts like a digital brain, connecting to a firm's data sources to automate information gathering and analysis.

Topics

AI Workspace Productivity Software

Mentioned in this video

softwareModel ML

An AI workspace for financial services that mimics human access to files, emails, CRM, data vendors, and public information, built on an agentic system.

companyFancy

A previous YC company founded by Arie and Chaz, which was a vertically integrated last-mile grocery delivery business. It was acquired by Gopuff.

companyFat Llama

A previous YC company founded by Arie and Chaz, which was a marketplace for renting items with insurance. It took three years to find product-market fit.

softwareCrunchbase

A platform used by Model ML's initial automated system to find comparable companies when evaluating investment opportunities.

personMichael Seibel

Former CEO of Y Combinator, who emphasized the need for rapid progress and frequent communication (daily or hourly check-ins) during the early stages of a startup.

organizationS&P

Mentioned as a source for company data that Model ML's initial system would query.

companyGopuff

Acquired the grocery delivery business Fancy 18 months after its launch, seeking to enter the European market.