AI Startup Model ML Secures $12M to Streamline Financial Workflows

Emerging from stealth mode, Model ML has secured $12 million in funding to address inefficiencies within financial and professional services. The funding round, led by Y-Combinator and London-based LocalGlobe, saw participation from angel investors linked to major banking and private equity firms. The startup aims to scale its AI-driven platform designed to eliminate repetitive tasks in regulated industries.

Initially targeting the financial sector, Model ML has developed solutions for common inefficiencies in professional workflows, particularly those that burden highly skilled professionals. On average, financial analysts, consultants, and strategists reportedly spend up to five weeks annually on tasks like data retrieval, analysis, and reporting. Traditional AI tools have fallen short in addressing these inefficiencies due to the complex and segmented nature of financial systems, as well as the strict compliance requirements in regulated sectors.

Model ML differentiates itself by offering an AI platform that adapts to an organization’s specific needs. Rather than relying on generic machine-learning models, the system builds a cognitive architecture designed to mirror how professionals access and process information. This tailored approach ensures real-time insights with a higher degree of accuracy, security, and compliance compared to general-purpose AI tools.

The platform integrates seamlessly into existing workflows, with an API enabling organizations to incorporate AI-driven insights directly into their tools. Additionally, Model ML offers a user-friendly interface that aims to replace traditional tools like Microsoft Office and Google Drive, creating a unified, AI-powered workspace for professional use. The goal is to streamline data-driven decision-making without the need for manual searches or complex processes.

A key feature of Model ML is its voice-controlled interface, which allows users to query financial data through simple voice commands. Accessible on both mobile and desktop devices, the system enables professionals to interact with it as easily as they would consult a colleague. This approach is expected to improve decision-making in high-pressure environments where time efficiency is critical.

With real-time conversational AI gaining momentum, particularly in sectors like finance and consulting, Model ML is positioning itself to meet the growing demand for faster, more reliable data access. The platform only retrieves data from pre-approved, trusted sources within the organization, ensuring that all information meets regulatory and security standards. Unlike many AI systems that struggle with accuracy in complex environments, Model ML is specifically designed to handle sensitive and highly regulated information.

Founded by brothers Chaz Englander and Arnie Englander, known for their previous ventures Fat Llama and Fancy, Model ML has already secured over 20 clients, including major financial institutions. Early adopters have reported improvements in productivity and efficiency, with some organizations reshaping their business strategies as a result of the platform’s capabilities.

Reflecting on the need for change, Chaz Englander, co-founder and CEO of Model ML, stated, “Traditional software requires manual data gathering for emails, financial models, and presentations—but that’s changing. Model ML has rebuilt these applications from the ground up on top of advanced AI agentic systems, eliminating the need for manual data collection.”

LocalGlobe Partner Ziv Reichert added, “Model ML is tackling one of AI’s biggest economic opportunities: enabling highly-paid knowledge workers to automate the vast amount of repetitive yet valuable manual work they do daily. The product is quickly changing expectations of both worker output and output quality.”

As it gains momentum, Model ML is poised to become a key player in reshaping how financial and professional services operate, offering a more efficient alternative to traditional data processing and decision-making workflows.