Daloopa Raises $47 Million In Series C Funding Round

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Daloopa has raised $47 million in Series C funding. This round was led by Brighton Park Capital, with participation from Squarepoint Capital, Touring Capital, and Nexus Venture Partners. It brings the company’s total funding to over $100 million.

What is Daloopa?

Daloopa provides structured, source linked financial data infrastructure for AI and agentic workflows in finance. The platform automates extraction from company filings and delivers auditable, standardized data covering 5,500+ global public companies with up to 14 years of history and 4-10x more data points per company than competitors.

Key differentiators include:

• Every data point is hyperlinked to its original source for full auditability and >99% accuracy.
• It supports analyst workflows (e.g., cutting 70% of time for new model builds and ~2 hours per ticker during earnings updates).
• It powers AI agents and LLMs through integrations like Model Context Protocol (MCP) connectors with OpenAI’s ChatGPT, Anthropic’s Claude, Perplexity, and Rogo.
• Delivery formats include Excel add-in, API, and cloud native options (Snowflake, Databricks, AWS S3).
• A new Partner API enables third party developers to build on the data.

The company targets hedge funds, asset managers, banks, and other institutions, with over 160 customers. It positions itself as the reliable data layer addressing hallucinations and inconsistencies in web sourced AI financial tools.

Funding Context and History

• Series C ($47M, May 2026): Led by Brighton Park Capital.
• Prior rounds: Included a $13M strategic investment (July 2025) focused on AI expansion and MCP launch, an $18M Series B (May 2024) led by Touring Capital with Morgan Stanley and Nexus, and earlier rounds (Series A ~$20M in 2021).
• Cumulative capital raised now exceeds $100M, reflecting strong investor conviction in the data infrastructure thesis amid AI adoption.

Brighton Park Capital, a New York-based growth equity firm, led the round. Partner Tim Drager highlighted Daloopa’s role in solving critical data challenges for production AI workflows. The firm was advised by Phil Hadley, former CEO and Chairman of FactSet. Squarepoint Capital’s participation adds quant expertise.

The round occurs as financial institutions shift from AI experimentation to production deployment. CEO Thomas Li notes that AI performance hinges on data quality, issues like misaligned fiscal calendars or inconsistent metrics can derail valuations, earnings analysis, and portfolio modeling. Daloopa’s structured dataset mitigates these risks where public web data falls short.

Recent milestones include:

• Benchmark showing AI agent accuracy gains of up to 71 percentage points with structured data.
• Rapid customer growth and revenue doubling in the past year.
• Expanding use cases beyond hedge funds into asset managers and banks.

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How will Daloopa use the funds?

Daloopa plans to:

• Accelerate platform expansion and data depth (more companies, granularity, faster updates).
• Develop AI native products, workflows, agents, and analytical tools on top of the core data infrastructure.
• Scale teams in engineering, product, data, and go to market.

Daloopa operates at the intersection of fintech data providers and AI infrastructure. Traditional players like FactSet or Bloomberg offer broad data but less AI native structuring and agentic focus. Newer AI focused entrants often lack Daloopa’s depth, auditability, and scale (5,500+ tickers with extensive history).

Strengths include proprietary AI for extraction/labeling, a perfection-oriented culture, and ecosystem integrations that embed it into analysts’ existing tools. Challenges in the space involve maintaining accuracy at scale and competing with larger incumbents’ resources. The funding validates its traction in a market where data quality is the primary bottleneck for reliable AI in high stakes finance.

This Series C positions Daloopa as a foundational player in the “AI agent ecosystem” for financial services. With production deployments accelerating, the company is poised for further revenue growth and potential expansion into adjacent areas like broader data partnerships or advanced analytics. Continued execution on data quality, integrations, and team scaling will determine its ability to capture a significant share of the growing demand for trustworthy financial data infrastructure.

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