Daloopa Raises $47 Million Series C to Power the Data Layer Behind AI-Driven Finance

WHY THIS MATTERS

The announcement on May 28, 2026, that Daloopa has closed a $47 million Series C funding round led by Brighton Park Capital marks a critical turning point in the commercialization of financial AI. Over the past two years, Wall Street and the broader buy-side ecosystem have aggressively integrated Large Language Models (LLMs) and autonomous AI agents into their research operations. However, firms have run into a brick wall: generalist AI models suffer from severe data accuracy and “hallucination” problems because they rely on scraped, unstructured web inputs. In high-stakes environments like valuation modeling, portfolio risk evaluation, and earnings tracking, a single unstandardized data definition or misaligned fiscal calendar can quietly corrupt an entire quantitative strategy.

Daloopa addresses this vulnerability by acting as the foundational, audit-ready data layer for financial machine learning. The platform covers more than 5,500 public companies globally, extracting up to 10 times more fundamental data points per company than traditional data aggregators. Crucially, every single metric is programmatically source-linked directly back to its original regulatory filing (such as SEC 10-K and 10-Q documents). Daloopa’s internal benchmark testing proves the massive value of this structural grounding: AI agents trained on their clean, structured database achieved up to a 71 percentage point improvement in processing accuracy over agents utilizing standard web-based semantic retrieval. By de-risking autonomous data collection, Daloopa is allowing hedge funds and institutional trading desks to safely transition AI systems out of experimental chat windows and directly into automated production workflows.

Daloopa, the essential data infrastructure for AI and agentic workflows in finance, today announced it has raised $47 million in Series C funding led by Brighton Park Capital, with participation from Squarepoint Capital, Touring Capital, and Nexus Venture Partners. 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 — and expand the company’s team across engineering, product, and go-to-market. 

As AI agents power financial workflows, the limiting factor is whether the systems are grounded in reliable data. In high-stakes use cases like valuation, earnings analysis, and portfolio modeling, even small inconsistencies such as misaligned fiscal calendars or inconsistent metric definitions can significantly impact outcomes. 

Before structured data infrastructure, analysts were forced to spend hours manually collecting and entering data from company filings, and validating whether each datapoint was correct. AI tools also face a data accuracy problem as most rely on web-sourced inputs that are not standardized or source-linked, inheriting those inconsistencies and producing outputs that are often unreliable. 

Daloopa addresses this by providing structured, source-linked financial data that investors can reliably use. The platform now covers over 5,500 public companies globally, delivers up to 10 times more data points per company than other providers, and each datapoint is linked to its original source for auditability. Investment firms trust Daloopa to power workflows ranging from quarterly analysis and scenario modeling to AI-assisted research and reporting. 

“We’re seeing firms move from early experimentation toward deploying AI in real investment workflows, and that changes the requirements entirely,” said Thomas Li, CEO of Daloopa. “It’s no longer enough for models to simply generate answers; they must be accurate and fully traceable. Our focus is on building the data infrastructure that makes that possible, so firms can trust what AI is producing.” 

The funding follows a series of recent product and partnership milestones that position Daloopa at the center of the emerging AI investment research stack. The company recently:

• Expanded access to its data through MCP connectors with OpenAI’s ChatGPT, Anthropic’s Claude, Perplexity, and Rogo – bringing structured financial data directly into the tools analysts already use
• Published a benchmark study showing AI agent accuracy improved up to 71 percentage points, when grounded in structured, auditable financial data versus web-based retrieval
• Added new platform capabilities, including programmatic access via API, as well as cloud-native delivery via Snowflake, Databricks, and AWS S3. 

Daloopa is also introducing a Partner API, unlocking third party product use cases. The new offering enables select partners and startup developers to leverage best-in-class financial data to build AI workflows and to reference within their product suite. 

Together, these developments underscore a broader shift in the market: the bottleneck in AI-driven finance is not model intelligence, but data infrastructure.  

“Daloopa is solving one of the most consequential data challenges in financial services,” said Tim Drager, Partner at Brighton Park Capital. “As AI becomes embedded in financial decision-making and core investment workflows, the firms that succeed will be those with the strongest data foundations. Daloopa has built exactly that and is already trusted by over 160 financial institutions, which speaks to both the quality of the platform and the urgency of this problem. We are thrilled to partner with Thomas and the team as they continue to define this category.” 

Brighton Park Capital was supported by Special Advisor, Phil Hadley, former CEO and Chairman of Factset in their evaluation of Daloopa.  

That momentum is also reflected in how investment firms are engaging with Daloopa. A growing number of customers are already using the platform to operationalize AI in production workflows, and in some cases, firsthand experience with the technology has also led to investment conviction.  

The company has also continued to scale rapidly, doubling revenue over the past year while expanding coverage and deepening integrations across the AI ecosystem. As firms push toward greater automation, Daloopa is becoming a core part of the infrastructure that enables AI and agentic workflows in financial services.

FF NEWS TAKE

Daloopa is executing a classic ecosystem land-grab by transforming itself from a traditional data vendor into an indispensable developer platform. Rather than attempting to build a standalone, proprietary software terminal to compete with entrenched monopolies like Bloomberg or FactSet, Daloopa is choosing to become the underlying plumbing for every financial tool on the market.

The strategy relies on their rapid deployment of Model Context Protocol (MCP) connectors. By launching plug-and-play MCP bridges directly into the world’s leading frontier AI applications—including Anthropic’s Claude, OpenAI’s ChatGPT, Perplexity, and specialized financial copilot Rogo—Daloopa ensures that when an investment analyst prompts an LLM for fundamental data, the model automatically pulls from Daloopa’s verified, auditable database. Furthermore, by expanding programmatic access via enterprise data clouds like Snowflake, Databricks, and AWS S3, alongside the debut of a new Partner API, Daloopa is creating an expansive data monopoly. They are enabling alternative fintech startups to build custom financial software entirely dependent on their backend. Backed by the deep expertise of Brighton Park Capital and special advisor Phil Hadley (the former CEO of FactSet), this Series C cash injection allows Daloopa to aggressively scale its engineering footprint and permanently anchor its code at the center of the emerging AI investment research stack.