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Daloopa has raised $54.0M across 4 funding rounds.
Key people at Daloopa.
Daloopa has raised $54.0M in total across 4 funding rounds.
Based in New York City, Daloopa provides an artificial intelligence-powered data infrastructure platform that extracts and organizes historical financial information from regulatory filings, transcripts, and key performance indicators for investment professionals. The enterprise software-as-a-service company delivers clean, hyperlinked, and customizable data sets covering more than 5,500 public companies globally with up to 14 years of historical records. Its core technology, which includes an automated Excel agent and direct application programming interface access, is utilized by hedge funds, private equity firms, and mutual funds to accelerate complex research workflows. Operating with a global workforce of approximately 300 employees, the enterprise has secured $40 million in total venture funding from prominent institutional backers including Touring Capital, Morgan Stanley, and Nexus Venture Partners. Daloopa was founded in 2019 by Thomas Li, Jeremy Huang, and Daniel Chen.
Daloopa has raised $54.0M in total across 4 funding rounds.
Daloopa's investors include Pavilion Capital, Priya Saiprasad, Craft Ventures, Hilarie Koplow-McAdams, Nexus Venture Partners, Single Fin Ventures, SoftBank Investment Advisers, Uncorrelated Ventures, UpHonest Capital, Morgan Stanley, Credit Suisse, 8090 Industries.
Daloopa is an AI-powered financial data platform that provides the most complete and accurate historical fundamental data for public companies, enabling investment professionals to build and update financial models faster directly in Excel.[2][4][6][7] Founded in 2019 and headquartered in New York with operations in Noida, India, it serves hedge funds, private equity firms, mutual funds, investment banks, and corporates by sourcing data from SEC filings, transcripts, presentations, and press releases—offering 4-10x more data points per company (covering nearly 5,000 global tickers) with >99% accuracy and full auditability via hyperlinks.[1][4][5][7] As a Series B company, Daloopa has raised $18M in 2023 (led by Touring Capital with Morgan Stanley and Nexus Venture Partners) and $13M in July 2025 (with Pavilion Capital), fueling AI enhancements, global expansion into Europe and Asia, and integrations like Model Context Protocol (MCP) with Anthropic's Claude for Financial Services.[2][4][5]
The platform solves the slow, error-prone manual extraction of financials, KPIs, guidance, and segmentals, accelerating model-building, earnings analysis, and AI-driven workflows for better investment insights.[3][6][7] Its growth momentum includes partnerships with leading buy-side/sell-side institutions, powering LLMs/OpenAI, and proven efficiency gains—like mutual funds reducing data discovery time during pilots.[2][4][8]
Daloopa was founded in 2019 by CEO Thomas Li, a former TMT equity research analyst at Point72 and KCL Capital (with finance/economics degrees from NYU), alongside two engineers from top tech firms.[1][2][6] Frustrated by the manual drudgery of parsing filings, building models, and tracking KPIs during his investing days, Thomas built what he wished existed: an AI tool to automate data extraction without losing accuracy or traceability.[6]
Early focus was on creating a proprietary AI parsing workflow for "comprehensive data extraction" (vs. template-based methods), delivering 13 years of hyperlinked historicals from day one.[6][7] Pivotal moments include rapid adoption by top hedge funds/PE firms, the 2023 Series B for product scaling, and 2025's $13M round to launch MCP and expand AI integrations amid surging LLM demand in finance.[2][4][5] This evolution from analyst pain-point solver to infrastructure for AI-enabled research has positioned it as a critical tool for global institutions.[2][6]
Daloopa rides the AI transformation of finance, where LLMs and agents demand high-quality, auditable fundamental data to scale research beyond human limits—addressing the "data integrity" bottleneck in AI adoption.[2][4][5] Timing is ideal post-2023 AI boom, as manual modeling can't match AI speed amid rising earnings complexity and global filings volume; market forces like regulatory scrutiny (e.g., audit trails) and PE/hedge fund digitization favor its 10x data density.[4][6][7]
It influences the ecosystem by powering top institutions' AI stacks (e.g., inflection detection, valuations), enabling faster alpha generation, and setting standards for "AI-ready" financial data—accelerating industry shift from spreadsheets to agentic workflows while competitors lag in completeness.[2][4][8]
Daloopa is primed to dominate as the foundational data layer for financial AI, with $13M fueling deeper LLM integrations, product expansion, and EMEA/Asia growth to capture rising demand from AI-scaling funds.[4][5] Trends like multi-agent research systems and real-time filings analysis will amplify its edge, potentially evolving it into a full AI research copilot platform. As AI becomes non-optional in investing, Daloopa's auditability moat positions it to compound efficiency gains into outsized influence—freeing analysts for high-value insights that drive superior returns, just as Thomas envisioned from his trading floor days.[6]
Daloopa has raised $54.0M across 4 funding rounds. Most recently, it raised $13.0M Other Equity in July 2025.
Key people at Daloopa.
