SUMM

Model ML is an AI workspace built specifically for financial services, offering an office-suite-like interface (Word, PowerPoint, Excel equivalents) powered by an agentic system mimicking a human's digital access in a firm.
The system integrates with files, emails, CRMs, data vendors, internal/external data, and public filings, aiming to save time on data gathering and analysis.
The platform uses a "cognitive architecture" to overlay user interface and automate tasks.
In the last seven days, Model ML signed as many contracts as in all of Q4, signaling rapid adoption and visible value.
10% of the largest private equity and investment banking firms use Model ML, as well as asset managers, sovereign wealth funds, and venture firms.

Previously, finance professionals used traditional office suites and Outlook, performing many manual, repetitive tasks.
Model ML automates routine work, such as creating earnings summaries from filings, reducing time from days to near-instant production.
The solution produces slides and reports that are 90-95% complete and sometimes more accurate due to the ability to pull from multiple data sources.
Many tasks, especially data gathering and presentation, are fully automated at top firms, with AI models showing higher accuracy than humans in structured data extraction.

In the past year, industry sentiment shifted from "testing" to "using" AI, resulting in actual contracts and deployment rather than pilots.
Improvements in agentic workflows, function calling, vision models, and OCR have advanced capabilities, like reading charts and tables automatically.
The perception that AI capabilities haven't changed much in the last six months is incorrect; they're already automating previously manual work.

Historically, financial institutions were slow to adopt new software, but in the last year, AI has become a top executive-level priority.
Decisions to purchase Model ML are made at CEO or firm leadership levels, with demos showing impactful real-use cases.
Building trust through in-person meetings and tailored demos is key, as adoption represents a significant personal responsibility for decision-makers.

Perseverance and passion are critical for startup founders.
Hiring is now focused more on cultural fit and genuine enjoyment of the work rather than just credentials.
The founders work seven days a week, fostering a strong work ethic and close-knit team.
Fat Llama was a peer-to-peer rental marketplace, and Fancy was a vertically integrated last-mile grocery delivery startup; each had different journeys to product-market fit.

Both startups faced unique challenges—such as theft concerns in Fat Llama and payment disruptions at Fancy—but learned resilience and the importance of direct customer feedback.
They handled customer support personally in the early stages, providing continuous and in-depth feedback.
The founders highlight the risk of losing customer touch as companies grow and emphasize the need to stay customer-centric.

Motivation shifted from making money to building impactful, widely-used products.
Even in B2B AI, having a B2C mindset—focusing on user impact and delight—is seen as critical.
The founders believe in maintaining familiar user interfaces, like Excel and PowerPoint, while automating underlying workflows, foreseeing a shift to fully autonomous processes this year.

Being siblings has helped with honest communication and trust between the founders.
Clear separation of responsibilities is important; their overlap centers on product and customer, driving collaboration in key areas.
Advice for choosing co-founders: prioritize compatibility, complementary skills, and aligned interests over overlapping expertise.

They advise young people to start companies if they're passionate and willing to persevere, but caution that it's hard work with uncertain rewards.
The worst-case scenario is rapid learning; other traditional jobs will remain available later.
Taking risks early in one’s career is easier than later when commitments increase.

People from finance/quant backgrounds may need to unlearn big-company habits, adopting faster, more iterative approaches essential in startups.
"First principles thinking" and rapid iteration are favored over process-heavy tradition.

YC's culture—focus on frequent metrics and instant support networks—is hard to replicate elsewhere and maintains a sense of urgency and community.
Participating in a predominantly AI-focused YC batch brought valuable collaboration and inspiration.

San Francisco is viewed as the ideal startup hub, offering unmatched community and work ethic.
However, Europe (particularly London) provides strong, less competitive engineering talent pools.
For European founders, moving to top-tier cities—ideally SF, or London if not feasible—is advised, and most traditional customers are US-based.

Founders split their time between New York, London, Hong Kong, and San Francisco to stay close to customers and decision-makers.
They recommend prioritizing proximity to ambitious peers and customers.
The conversation ends with gratitude and encouragement for aspiring founders to apply to YC and consider starting companies early.

The AI Finance Startup Taking On Microsoft Office