Daloopa, a New York-based financial data infrastructure company, has closed a $47 million Series C to support its work supplying investment firms with reliable data for AI-powered workflows. The company's core argument is straightforward: an AI system in finance is only as useful as the data feeding it. CEO and co-founder Thomas Li knows this from experience. Before starting Daloopa, he worked as a TMT analyst at Point72 and later at KCL Capital, spending significant time manually pulling data from company filings and checking figures before he could get to the actual investment work.
According to the 2026 Global AI in Financial Services Report from the Cambridge Centre for Alternative Finance, 81% of surveyed financial services firms are now adopting AI at some level, with 40% reporting advanced adoption. Widespread use, however, does not mean reliable use. Most AI tools in finance still draw on web-sourced data that is neither standardized nor traceable to original documents, which introduces errors that matter when the decisions involve real money.
Brighton Park Capital led the round, with Squarepoint Capital, Touring Capital, and Nexus Venture Partners also participating. The funding will go toward hiring in engineering, product, and go-to-market, while accelerating platform growth as firms move AI systems from testing into live operations.
The problem Daloopa is addressing is specific. When analysts build valuation models or work through earnings analysis, small inconsistencies in the underlying data — a misaligned fiscal calendar, a metric defined differently across sources — can quietly distort the results. For years, analysts handled this by collecting and entering data by hand, filing by filing. AI tools that pull from unstructured web sources carry the same inconsistencies forward, just faster and at larger volume.
Daloopa's platform covers more than 5,500 public companies globally and links every data point back to its original source document. That traceability allows analysts and AI systems to check where a number came from. The company reports delivering up to ten times more data points per company than other providers, with an average accuracy rate above 99% across millions of data points.
Auditability has become a serious requirement as investment firms weigh whether to let AI systems participate in actual decisions. As Databricks noted in its 2026 financial services outlook, agentic AI — where software systems plan and carry out multi-step tasks automatically — only works reliably when governance, data lineage, and observability are built into the process from the start. Daloopa's platform is structured around those principles.
"We're seeing firms move from early experimentation toward deploying AI in real investment workflows, and that changes the requirements entirely. 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." — Thomas Li, CEO and co-founder of Daloopa
The new capital will be directed at three areas: engineering, product development, and go-to-market. Revenue doubled over the past year, and the company is using this round to keep pace with growing demand while expanding how its data connects to other tools in the market.
Recent product additions include programmatic access via an API and cloud delivery through Snowflake, Databricks, and AWS S3. Daloopa also extended its data into tools analysts already use daily, through MCP connectors with OpenAI's ChatGPT, Anthropic's Claude, Perplexity, and Rogo. MCP, or Model Context Protocol, is a standard that lets AI tools call external data sources mid-task. In practical terms, analysts can now access Daloopa's structured financial data without leaving their primary research environment.
The company is also launching a Partner API. This will allow selected third-party developers and startups to build their own AI-powered financial tools using Daloopa's data, extending the platform's reach beyond direct customers.
Thomas Li co-founded Daloopa with Daniel Chen, now Chief Revenue Officer, and Jeremy Huang, Chief Research Officer. The team also included engineers from top tech companies. Li's starting point was his own frustration as an analyst: too much working time went into sourcing and cleaning data, leaving less time for the analysis itself. The goal from the beginning was to remove that friction without trading away accuracy.
The platform today serves hedge funds, mutual funds, investment banks, and equity research teams. It covers more than 5,500 public companies and carries 14 years of historical data. According to the company, it reduces the time needed to build a new financial model by up to 70% and saves analysts an average of two hours per company during earnings season.
More than 160 financial institutions use Daloopa. Anthropic, OpenAI, and Perplexity are also listed as users of its data infrastructure. An internal benchmark study found that AI agent accuracy improved by up to 71 percentage points when working from Daloopa's structured, source-linked data rather than standard web retrieval.
Brighton Park Capital led the Series C. The firm focuses on entrepreneur-led, growth-stage companies in software, healthcare, and technology-enabled services. Its assessment of Daloopa was supported by Phil Hadley, former CEO and Chairman of FactSet, who acted as a special advisor on the investment. Squarepoint Capital, Touring Capital, and Nexus Venture Partners also joined the round, with Touring Capital and Nexus Venture Partners returning from previous funding.
"Daloopa is solving one of the most consequential data challenges in financial services. 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." — Tim Drager, Partner at Brighton Park Capital
