Thomas Li was working at Point72, the hedge fund founded by notorious investor Steve Cohen, when he realized that the financial industry relies heavily on manual data entry processes that could be prone to errors.

“As a buy-side analyst, I felt the pain of manually sourcing and entering data to build and update financial models,” Li told TechCrunch. “It took time away from the more important work of analyzing and making investments.”

After meeting Jeremy Huang, a former software engineer at Airbnb and Meta, and Daniel Chen, an ex-Microsoft engineer, through New York University connections (all three are all alums), Li decided to try his hand at an automated solution to the data entry challenges.

The three partners  launched Daloopa, which uses AI to extract and organize data from financial reports and investor presentations for analysts. Daloopa on Tuesday announced that it 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,” Li said. “This way of approaching the data discovery process keeps highly competitive firms and teams ahead of the curve.”

Daloopa’s customers are primarily hedge funds, private equity firms, mutual funds and corporate and investment banks, Li says. They use the startup’s tools to build workflows for investment and due diligence research. The workflows, powered by AI algorithms, discover and deliver data to analysts’ financial models, reducing the need to copy data manually.

“Daloopa provides a new way to get mission-critical data to both the buy side and sell side,” Li said. “The time savings is reinvested into research and analysis, or client-facing time — helping our customers gain an edge in their research process.”

Now, I’m a little skeptical that Daloopa’s AI doesn’t make mistakes: No AI system’s perfect, after all. Thanks to the phenomenon known as hallucination, it’s not uncommon for AI models to make up facts and figures when summarizing documents and files.

Li didn’t suggest that Daloopa is foolproof. But he did claim that the platform’s algorithms “only continue to improve over time” as they’re trained on growing sets of financial documents. Mum’s the word on where the data’s sourced from, exactly; Li says it’s a trade secret.

“Daloopa has been an AI company since birth five years ago, before all the AI hype,” Li said. “We’ve spent those years training our algorithms and developing AI for financial institutions.”

With the new funding, which brings NYC-based Daloopa’s total raised to $40 million, the company plans to grow its team of ~300 employees, bolster product R&D and expand its customer acquisition efforts.

“Daloopa is an AI-powered solution that started ahead of the curve and has seen year-over-year growth acceleration over the past two years,” he said. “As financial institutions increase their adoption of AI tools, we’re very well positioned to be a leader in the AI-driven fundamental data space.”

Thomas Li was working at Point72, the hedge fund founded by notorious investor Steve Cohen, when he realized that the financial industry relies heavily on manual data entry processes that could be prone to errors.

“As a buy-side analyst, I felt the pain of manually sourcing and entering data to build and update financial models,” Li told TechCrunch. “It took time away from the more important work of analyzing and making investments.”

After meeting Jeremy Huang, a former software engineer at Airbnb and Meta, and Daniel Chen, an ex-Microsoft engineer, through New York University connections (all three are all alums), Li decided to try his hand at an automated solution to the data entry challenges.

The three partners  launched Daloopa, which uses AI to extract and organize data from financial reports and investor presentations for analysts. Daloopa on Tuesday announced that it 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,” Li said. “This way of approaching the data discovery process keeps highly competitive firms and teams ahead of the curve.”

Daloopa’s customers are primarily hedge funds, private equity firms, mutual funds and corporate and investment banks, Li says. They use the startup’s tools to build workflows for investment and due diligence research. The workflows, powered by AI algorithms, discover and deliver data to analysts’ financial models, reducing the need to copy data manually.

“Daloopa provides a new way to get mission-critical data to both the buy side and sell side,” Li said. “The time savings is reinvested into research and analysis, or client-facing time — helping our customers gain an edge in their research process.”

Now, I’m a little skeptical that Daloopa’s AI doesn’t make mistakes: No AI system’s perfect, after all. Thanks to the phenomenon known as hallucination, it’s not uncommon for AI models to make up facts and figures when summarizing documents and files.

Li didn’t suggest that Daloopa is foolproof. But he did claim that the platform’s algorithms “only continue to improve over time” as they’re trained on growing sets of financial documents. Mum’s the word on where the data’s sourced from, exactly; Li says it’s a trade secret.

“Daloopa has been an AI company since birth five years ago, before all the AI hype,” Li said. “We’ve spent those years training our algorithms and developing AI for financial institutions.”

With the new funding, which brings NYC-based Daloopa’s total raised to $40 million, the company plans to grow its team of ~300 employees, bolster product R&D and expand its customer acquisition efforts.

“Daloopa is an AI-powered solution that started ahead of the curve and has seen year-over-year growth acceleration over the past two years,” he said. “As financial institutions increase their adoption of AI tools, we’re very well positioned to be a leader in the AI-driven fundamental data space.”