Thomas Li worked at Point72, the hedge fund he founded infamous investor Steve Cohenwhen he realized that the financial industry relies heavily on manual data entry processes that could be liable to errors.
“As a buy-side analyst, I felt the pain of manually sourcing and entering data to construct and update financial models,” Li told TechCrunch. “It took time away from the more essential work of analyzing and making investments.”
After Li met Jeremy Huang, a former software engineer at Airbnb and Meta, and Daniel Chen, a former Microsoft engineer, through connections at New York University (all three are graduates), he decided to work on an automatic solution for the Data to try entry challenges.
The three partners began Daloopa, which uses AI to extract and organize data from financial reports and investor presentations for analysts. Daloopa announced Tuesday that it has raised $18 million in a Series B funding round led by Touring Capital with participation from Morgan Stanley and Nexus Venture Partners.
“Daloopa is an AI-powered historical data infrastructure for analysts,” said Li. “This approach to the information discovery process ensures competitive corporations and teams stay one step ahead.”
Daloopa's customers primarily include hedge funds, private equity firms, mutual funds, and company and investment banks, says Li. They use the startup's tools to create workflows for investment and due diligence research. The AI ​​algorithm-based workflows discover and deliver data to analysts' financial models, reducing the necessity to manually copy data.
“Daloopa offers a brand new method to deliver business-critical data to each the buy and sell sides,” said Li. “The time saved is reinvested in research and evaluation, or the time we spend in touch with the shopper remain – which helps our customers get a head start of their research process.”
Now I'm slightly skeptical as as to whether Daloopa's AI doesn't make mistakes: After all, no AI system is ideal. This is just not unusual in AI models because of the phenomenon often called hallucination Compile facts and figures when combining documents and files.
Li didn’t claim that Daloopa is foolproof. However, he claimed that the platform's algorithms “proceed to enhance over time” as they’re trained on increasing volumes of economic documents. Mom knows exactly where the information comes from. Li says only that it comes from “public sources akin to SEC filings and investor presentations.”
“Daloopa has been an AI company because it was founded five years ago, before all of the AI ​​hype,” said Li. “We have spent those years training our algorithms and developing AI for financial institutions.”
With the brand new funding, which brings New York-based Daloopa's total revenue to $40 million, the corporate plans to grow its team of roughly 300 employees, strengthen product research and development and expand its customer acquisition efforts.
“Daloopa is an AI-powered solution that has been ahead of the curve and has seen year-on-year growth during the last two years,” he said. “As financial institutions increasingly adopt AI tools, we’re thoroughly positioned to be a frontrunner in AI-driven fundamental data.”
