Most funding announcements are noise. A press release drops, a few newsletters pick it up, and by Thursday nobody remembers the name. But when Daloopa secures $47M in a Series C round, something feels different. This is not a company chasing a trend. This is a company that has been quietly solving a problem most people in finance did not even want to admit existed: the data underneath their AI tools is, frankly, a mess. And now the market is catching up to what Daloopa has been building for seven years.
So here is what you actually need to know.
What Is Daloopa and What Does It Do?
Daloopa is a New York City-based company building essential data infrastructure for AI and agentic workflows in finance. Think of it this way. AI in finance is only as smart as the data you feed it. Bad data in, bad decisions out. Simple as that.
The platform automates extraction from company filings and delivers auditable, standardized data covering 5,500 plus global public companies with up to 14 years of history and 4 to 10 times more data points per company than competitors. Every single number links back to its original source. You can check it. You can verify it. You can trust it.
Led by CEO Thomas Li, Daloopa sources, structures, and distributes a comprehensive historical financial dataset, empowering analysts to save valuable time and accelerate their decision-making.
That is the product. Clean data. Structured data. Data that does not embarrass you in front of a portfolio manager.
Who Invested in Daloopa’s $47M Round?
The investor lineup here is worth paying attention to. Because who writes the check tells you a lot about whether this is real or just well-packaged hype.
Daloopa raised $47 million in Series C funding led by Brighton Park Capital, with participation from Squarepoint Capital, Touring Capital, and Nexus Venture Partners.
Brighton Park Capital partner Tim Drager highlighted Daloopa’s role in solving critical data challenges for production AI workflows. The firm was also advised by Phil Hadley, the former CEO and Chairman of FactSet. That last name matters. FactSet is not a startup. It is one of the most trusted names in financial data. When someone with that background is advising your round, people in finance take notice.
And then there is Squarepoint. Squarepoint Capital is a firm that lives and breathes quantitative, data-driven research. These are not generalist VCs placing bets. These are people who use data every single day to make high-stakes decisions. Their participation is as close to a product endorsement as you can get in this industry. This Series C brings Daloopa’s total funding to over $100 million. That is not a small number. That is a company with real conviction behind it.
Why Did Daloopa Raise $47 Million?
Here is the kicker. The reason Daloopa secures $47M right now has everything to do with a problem that most people in the AI space are still tiptoeing around.
AI tools face a data accuracy problem because most rely on web-sourced inputs that are not standardised or source-linked. Think about what that actually means in practice. An AI agent runs an earnings analysis. It pulls numbers from somewhere on the internet. Those numbers might be slightly off. The fiscal calendar might not match. The metric definition might differ from what you thought.
In high-stakes use cases like valuation, earnings analysis, and portfolio modelling, even small inconsistencies such as misaligned fiscal calendars or inconsistent metric definitions can significantly impact outcomes. Small error. Big consequence. That is the world these firms are operating in.
The funding arrives as investment firms transition AI from experimental use cases into live production workflows, where the standard of data accuracy becomes far more consequential. The reality is, the “let’s experiment with AI” phase is over. Firms are now building workflows that real money flows through. And that changes everything about what acceptable data quality looks like.
How Daloopa Uses AI to Automate Financial Data
This is where things get genuinely interesting. Because Daloopa is not just storing data. It is changing how analysts actually spend their time.
The platform supports analyst workflows by cutting 70% of time for new model builds and saving approximately 2 hours per ticker during earnings updates. Two hours per ticker. If you are covering 30 companies, that adds up fast. That is real time handed back to analysts to do the thinking that actually matters, instead of copying numbers from PDFs.
But here is what actually separates Daloopa from tools that simply claim to be accurate. Every data point is hyperlinked to its original source for full auditability, with greater than 99% accuracy. You are not just told a number. You are shown exactly where it came from. That is a different level of accountability.
The company recently expanded access to its data through Model Context Protocol connectors with OpenAI’s ChatGPT, Anthropic’s Claude, Perplexity, and Rogo, embedding structured financial data directly into tools analysts already use day-to-day. And that integration strategy is smart. Nobody wants to learn a new tool. Daloopa goes where analysts already are. So adoption is not a fight. It is a natural extension of existing habits.
What Will Daloopa Do With the New Funding?
When a company raises $47M and you ask where the money is going, you want a specific answer. Not corporate speak. Daloopa has been fairly direct about it.
This funding will accelerate Daloopa’s platform growth as investment firms increasingly move AI systems from experimentation into production workflows, where accuracy and reliability are non-negotiable. It will also expand the company’s team across engineering, product, and go-to-market. Three areas. Engineering. Product. Sales. That is a company preparing to scale, not a company still figuring out what it is building.
The company has already doubled its revenue over the past year while expanding coverage and deepening integrations across the AI ecosystem. So this raise is not about survival. It is about acceleration. Big difference.
The founding thesis has always been simple. The best data wins in finance. Seven years ago that sounded philosophical. Right now it sounds like a business plan that is working.
How Daloopa Competes With Other Fintech AI Tools
Let’s be real. Whenever Daloopa secures $47M, the first thing competitors do is go look at their own positioning and wonder if they have a problem.
Daloopa Technologies ranks 1st amongst 183 active competitors, of which 25 are funded, and stands 2nd in terms of total funding among its competitors. That is a strong place to be in a crowded space. But rankings alone do not keep customers. What keeps customers is being genuinely hard to replace. And that is where Daloopa has built something durable.
Most financial data tools, and most general-purpose AI tools for that matter, do not source-link their data. A number appears in your model, and if you want to verify it, you are going back to a filing yourself. Manually. That is the old way.
The platform now covers over 5,500 public companies globally and delivers up to 10 times more data points per company than other providers. More data. Better sourced. And built specifically for the kind of AI workflows investment firms are now deploying in production. So. Once a firm integrates Daloopa into their stack, switching becomes painful. That is not a criticism. That is a sign of a product that actually works.
Is Daloopa Worth Watching in 2026?
Short answer: yes. Longer answer: here is why.
Prior to this Series C, Daloopa had already raised a $13M strategic investment in July 2025 focused on AI expansion and MCP launch, an $18M Series B in May 2024 led by Touring Capital with Morgan Stanley and Nexus, and a Series A of approximately $20M in 2021.
That is a consistent funding story built over several years. No sudden jumps. No pivots that make you nervous. Just a company that kept doing the work, kept growing, and kept raising on the back of real results. The announcement on May 28, 2026, that Daloopa has closed a $47 million Series C marks a critical turning point in the commercialization of financial AI.
The reality is, every serious conversation in finance right now ends up in the same place. AI deployments are only as good as the data powering them. The firms that figure out the data layer first will move faster, make better decisions, and leave slower firms behind. It is not a prediction. It is already happening. Daloopa is not waiting for the industry to catch up. It has been building the foundation for years. And now, with Daloopa securing $47M in fresh capital, it is ready to run.
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Hi Friends, This is Swapnil; I love reading and sharing knowledge. Currently working as a content writer at startupsunion.com. You all can hang out with me here.