AI Data Firm Daloopa Raises $20 Million Series A

By Sanford Bragg July 27, 2021

New York-based Daloopa, which offers AI-powered data extraction services, closed a Series A funding of $20 million led by Credit Suisse Asset Management’s NEXT Investors. The raise will be used to expand its product offering and add more sales and marketing staff. Instead of using AI to create novel datasets, as is the case with alternative data firms, Daloopa is seeking to disrupt the established financial statement data providers with new technology.

NEXT Investors is a private equity unit focused on fintech investments within Credit Suisse Asset Management.  Besides Daloopa, NEXT Investors has also invested in alternative data firms Thinknum and Dataminr.  Daloopa’s funding round had participation from Nexus Venture Partners – which had led Daloopa’s pre-seed and seed funding rounds – as well as Uncorrelated Ventures and Hack VC.  Daloopa’s total funding has been $24 million.

Daloopa is using the new funding to aggressively add staff, in particular salespeople and account managers.  Employees registered on LinkedIn have increased 72% over the last four months.  It is currently recruiting for a salesperson familiar with the equity research process who will prospect senior buy-side contacts (CIO, Analyst, PM, Director of Research).  It is also seeking a senior marketer to lead “global marketing strategy including advertising campaigns and branding.” 

The firm says it will also be spending on its tech stack and R&D for product development.  The company is expanding its data extraction capabilities to non-US financial filings, saying it “looks forward to extracting detailed financial fundamentals from the financial filings of all public companies globally.”

When we last profiled the company in May 2020, coverage was around 300 stocks.  According to CEO Thomas Li, coverage is now in ‘the thousands’. “For each company, we will cover every KPI, guidance, financial data with flawless accuracy,” he said.  Data is accessible through Excel plug-ins or through APIs.  

The company has ambitions to expand its data extraction beyond financial filings to a broader array of sources.  “Once we are completed with covering public companies, we plan on extending our technology across many different types of documents, including financial disclosures, regulatory filings, private financials, fund disclosure reports, and a variety of bank process related documents,” Li said.

Daloopa’s data extraction process is based on the steps that a buy-side analyst follows to model company financials. “Our algorithms are often codified versions of the process a senior associate at a hedge fund might use to train a new hire,” said Li. Customers specify data sets of interest and have them delivered the next day.

Daloopa was co-founded in 2019 by Li, a former buy-side TMT analyst; Jeremy Huang, a former AirBnB engineer; and Daniel Chen a former distributed systems researcher at Microsoft’s cloud division, Azure. The firm uses data analysts in India to validate data and train the data collection models.

Our Take

Daloopa’s capital raise gives some validation to its premise of applying AI to fundamental data collection.  Unlike alternative data firms and AI-driven ESG data firms which are using AI to extract data from novel sources, Daloopa is focused on one of the most competitive varieties of market data, seeking to differentiate itself on data quality (it also has a cost advantage, as we have noted previously).    

Not only do major market data vendors such as Bloomberg, FactSet and S&P Global offer financial statement data, but also relative newcomers like TagniFi, which competes on cost thanks to its reliance on XML-based financial filings.   Three months ago, Canalyst, a Vancouver-based platform offering financial models, began pivoting from being a model provider into a provider of fundamental data as it hired a senior veteran of CapIQ and Bloomberg. 

Despite the competition, Daloopa appears focused on its core proposition, now with greater capital resources for expanding its product and scaling its customer base. 

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