Daloopa Leverages AI to Streamline Financial Analysts’ Workflows for Enhanced Efficiency

Thomas Li was working at Point72, the hedge fund founded by renowned investor Steve Cohen, when he noticed a critical issue: the financial industry is heavily reliant on manual data entry, a process fraught with potential errors.

“As a buy-side analyst, I experienced firsthand the challenges of manually sourcing and entering data to construct and update financial models,” Li remarked. “This tedious process diverted my attention from the more crucial tasks of analysis and investment decision-making.”

After connecting with Jeremy Huang, a former software engineer at Airbnb and Meta, and Daniel Chen, a former engineer at Microsoft— all of whom share New York University as their alma mater— Li decided to tackle the data entry dilemma with an automated solution.

Together, the trio founded Daloopa, a company that harnesses AI technology to extract and organize data from financial reports and investor presentations for analysts. Recently, Daloopa announced the successful completion of an $18 million Series B funding round led by Touring Capital, with contributions from Morgan Stanley and Nexus Venture Partners.

“Daloopa is an AI-driven historical data infrastructure designed for analysts,” Li explained. “By innovating the data discovery process, we help competitive firms stay ahead in the market.”

Daloopa’s client base largely comprises hedge funds, private equity firms, mutual funds, and corporate and investment banks. According to Li, they utilize the startup’s tools to create workflows essential for investment analysis and due diligence research. These AI-powered workflows streamline the process of retrieving and delivering data to financial models, drastically reducing the need for manual data entry.

“Daloopa provides a transformative approach to accessing mission-critical data for both the buy side and sell side,” Li noted. “The time saved can be redirected towards in-depth research and client engagement, giving our customers a distinct advantage in their analytical processes.”

However, I remain slightly skeptical about Daloopa’s AI capabilities being without faults— no AI system is entirely flawless. The phenomenon known as hallucination often leads AI models to fabricate facts and figures when summarizing documents.

Li doesn’t claim that Daloopa is infallible, but he does assert that the platform’s algorithms “continue to improve over time” as they are trained on an expanding collection of financial documents. While the specifics of data sourcing remain undisclosed, Li mentions that the information is derived from “public sources such as SEC filings and investor presentations.”

“Daloopa has been an AI-focused company since its inception five years ago, long before the current AI boom,” Li said. “Over these years, we have dedicated ourselves to refining our algorithms and developing AI solutions tailored for financial institutions.”

With this latest funding, which increases Daloopa's total capital raised to $40 million, the New York City-based company plans to expand its team of approximately 300 employees, enhance product research and development, and intensify customer acquisition efforts.

“Daloopa is an AI-powered solution that was ahead of the game and has demonstrated year-over-year growth acceleration over the past two years,” he stated. “As financial institutions increasingly embrace AI technologies, we are well-positioned to lead in the AI-driven fundamental data landscape.”