Daloopa Raises $47M Series C as Finance AI Collides With a Data Problem

Daloopa, a New York-based financial data infrastructure company founded by Thomas Li, Daniel Chen, and Jeremy Huang, has raised $47M in Series C funding led by Brighton Park Capital, with participation from Squarepoint Capital, Touring Capital, and Nexus Venture Partners. The company says the new capital will be used to accelerate platform growth and expand teams across engineering, product, and go-to-market functions.

Daloopa provides structured, source-linked financial data designed for AI and agentic workflows in finance. The platform covers more than 5,500 public companies and is used by more than 160 financial institutions. The funding arrives at a moment when financial institutions are moving beyond AI experimentation and into production deployment, shifting the conversation from model capability to data reliability.

The broader implication is simple. The winners of the next phase of financial AI may not be the companies building the loudest models. They may be the companies supplying the most trusted data, creating the foundation that allows AI systems to operate inside high-stakes financial environments where accuracy matters more than novelty.

What Happened

Daloopa announced a $47M Series C led by Brighton Park Capital, with participation from Squarepoint Capital, Touring Capital, and Nexus Venture Partners. The round pushes Daloopa's total funding to more than $100M, according to the company. Daloopa previously raised an $18M Series B in 2024, reflecting continued investor confidence as financial institutions accelerate AI adoption.

Founded in 2019, Daloopa was created by Thomas Li, Co-founder and CEO, alongside Co-founders Daniel Chen and Jeremy Huang. The company has spent the past several years tackling one of finance's least glamorous and most expensive problems: turning mountains of financial filings, earnings reports, and company disclosures into structured, auditable data that can be trusted by both people and machines.

That may not sound exciting until you consider what happens when a hedge fund analyst, portfolio manager, private equity associate, or AI agent pulls the wrong number from a filing. The financial industry has a long history of discovering that bad data is usually invisible right up until it becomes extremely expensive. Daloopa's pitch is that every datapoint should be traceable back to its original source, and in a market increasingly filled with AI-generated answers, traceability is becoming a feature investors are willing to pay for.

Why This Matters

The AI market has developed a curious habit. Much of the attention flows toward models, copilots, agents, and interfaces. Investors debate which model is smartest, enterprises compare benchmarks, and social media turns every product launch into a heavyweight title fight. Meanwhile, data sits quietly underneath the entire stack like the foundation of a skyscraper. Nobody admires the concrete and nobody puts the concrete on stage, yet without it the building becomes an expensive physics experiment.

Daloopa's growth suggests financial institutions are beginning to recognize this reality. The company says revenue doubled over the past year while expanding adoption across more than 160 financial institutions. Those numbers matter because they indicate movement beyond curiosity. Experimentation asks whether AI can perform a task. Production asks whether the answer can be trusted. Those are very different questions.

Market Context

Financial services has become one of the most important proving grounds for enterprise AI. The opportunity is obvious. Analysts spend enormous amounts of time collecting information, validating figures, updating models, reviewing earnings transcripts, and preparing research. The risk is equally obvious. A chatbot recommending a bad restaurant is an inconvenience. A financial model built on incorrect data can influence investment decisions involving millions or billions of dollars.

This is why Daloopa's positioning around source-linked and auditable financial data is gaining attention. The company is not selling AI magic. It is selling confidence in the underlying information. As financial institutions move AI systems from pilot projects into production environments, confidence increasingly becomes a competitive advantage rather than a technical preference.

Daloopa has expanded access across the emerging AI ecosystem through integrations and connectors with ChatGPT, Claude, Perplexity, and Rogo. The company has also broadened delivery options through Snowflake, Databricks, and AWS S3 while introducing a Partner API that allows developers and partners to build products and workflows on top of its financial data infrastructure. Rather than forcing organizations to change how they work, Daloopa is positioning itself inside the workflows firms already rely on.

Competitive Landscape

The financial data market is not exactly known for being empty. Bloomberg, FactSet, S&P Global, Morningstar, and numerous specialized providers have spent decades building data businesses around financial professionals. What makes the current moment different is the emergence of agentic workflows and AI-powered research environments that require data to serve both humans and machines simultaneously.

Traditional financial data providers were built primarily for human consumption. The next generation of infrastructure providers is increasingly being optimized for machine consumption as well. Analysts no longer want only dashboards. They want agents capable of performing research tasks. Portfolio managers want systems that can surface insights quickly while maintaining accountability. Compliance teams want answers they can verify. The value shifts from simply possessing information to delivering information that can survive scrutiny, and Daloopa's focus on structured, source-linked data places it directly inside that transition.

What This Signals

The most interesting part of Daloopa's funding round may not be the size of the round itself. It is what the round says about where investors believe value will accumulate. Over the past several years, AI capital has flooded toward foundation models and applications. Those investments remain important, but infrastructure is increasingly attracting attention because infrastructure tends to benefit regardless of which application ultimately wins.

The market is gradually separating AI excitement from AI implementation. Implementation requires governance, reliability, auditability, and data. That is where Daloopa operates. The company's benchmark research reported up to a 71 percentage-point improvement in AI agent accuracy when grounded in structured financial data rather than web-based retrieval, highlighting a challenge enterprises face as AI systems move from experimentation into production environments.

The Bigger Industry Shift

The AI economy is entering a new phase. The first phase rewarded experimentation. The second phase rewarded adoption. The next phase is likely to reward trust. Financial institutions are among the largest buyers of enterprise AI infrastructure globally, but adoption only scales when accuracy, transparency, and accountability improve alongside model performance.

That trend extends far beyond finance. Healthcare, cybersecurity, enterprise software, and regulated industries are all confronting versions of the same question: How do you scale intelligence without sacrificing confidence in the answer? The challenge is becoming less about building smarter models and more about ensuring those models operate on information that can be verified.

Daloopa's Series C suggests investors believe trustworthy data infrastructure will become a critical part of that equation. The market has spent years asking what AI can do. Increasingly, the more important question is whether the information feeding those systems deserves to be trusted in the first place.

Frequently Asked Questions

What is Daloopa?

Daloopa is a New York-based financial data infrastructure company that provides structured, source-linked financial data for analysts, investment firms, and AI-driven finance workflows.

How much funding has Daloopa raised?

Daloopa says it has raised more than $100M in total funding, including a $47M Series C round announced in May 2026.

Who founded Daloopa?

Daloopa was founded in 2019 by Thomas Li, Daniel Chen, and Jeremy Huang.

Who led Daloopa's Series C funding round?

Brighton Park Capital led Daloopa's $47M Series C, with participation from Squarepoint Capital, Touring Capital, and Nexus Venture Partners.

What does Daloopa do?

Daloopa transforms financial filings, earnings reports, and disclosures into structured, source-linked datasets used by financial institutions and AI systems.

How many companies does Daloopa cover?

Daloopa says its platform covers more than 5,500 public companies globally.

Which AI platforms integrate with Daloopa?

Daloopa has announced integrations and connectors with ChatGPT, Claude, Perplexity, and Rogo, while supporting delivery through Snowflake, Databricks, and AWS S3.

What will Daloopa do with the new funding?

Daloopa plans to expand engineering, product, and go-to-market teams while accelerating platform growth and expanding its financial AI infrastructure platform.