How Daloopa is combining financial data, artificial intelligence and auditability to help investment professionals work faster and build more reliable AI-driven workflows
Artificial intelligence is changing how financial professionals research companies, build models and analyze markets. But as AI becomes more deeply integrated into finance, another challenge becomes increasingly important: the quality and reliability of the data behind those systems.
Daloopa is addressing that challenge by building financial data infrastructure designed specifically for investment research and AI-powered workflows.
Based in New York, Daloopa provides structured, source-linked financial data covering more than 6,000 public companies globally. The company's platform is designed to help analysts spend less time collecting and organizing financial information and more time analyzing it.
Turning Financial Documents Into Usable Data
Financial analysts traditionally spend significant amounts of time reviewing company filings, earnings materials, transcripts and other documents before the information can be used in financial models.
Daloopa uses artificial intelligence to automate much of this process.
Its technology extracts and structures financial and non-financial information, while linking individual data points back to their original sources. This creates an audit trail that allows analysts to verify where information came from rather than relying on an unexplained AI-generated answer.
The company says its platform contains 14 years of historical data and provides substantially more data points per company than many conventional sources.
AI Needs Reliable Data
One of Daloopa's central ideas is that financial AI is only as useful as the information supporting it.
Investment decisions can depend on relatively small differences in financial metrics, reporting periods or company definitions. For that reason, Daloopa has built its platform around source-linked and auditable information.
The company says every number in its database can be traced back to its original source, while its AI models are used throughout the data extraction and delivery process.
This approach has also positioned Daloopa as an infrastructure provider for other AI systems, rather than simply another financial research application.
Expanding Into AI Workflows
Daloopa has been expanding its technology beyond traditional investment research.
Its product portfolio includes Daloopa Scout, an AI assistant designed for financial modeling in Excel, as well as API and Model Context Protocol integrations that allow AI applications to access Daloopa's structured financial data.
In 2026, the company announced integrations involving OpenAI, Microsoft 365 Copilot and Google Cloud's Gemini Enterprise for Financial Services. These integrations are designed to bring source-linked financial information directly into AI-powered workflows.
The company's MCP infrastructure is designed to work across different AI platforms, allowing financial institutions to use Daloopa's data within their preferred AI environments.
Continued Investment in Financial AI
Daloopa's expansion has attracted significant investment.
In May 2026, the company announced a $47 million Series C funding round led by Brighton Park Capital, with participation from Squarepoint Capital, Touring Capital and Nexus Venture Partners.
The company said the funding would support continued development of its platform as financial institutions move AI systems from experimentation toward production-level workflows.
Daloopa says its technology is used by analysts at more than 185 large hedge funds, mutual funds and investment banks, while several major AI companies also use its infrastructure. These figures are company-reported.
Building Trust Into Financial AI
The next phase of AI in finance will require more than faster answers. Financial professionals also need to know where those answers come from and whether they can be independently checked.
Daloopa's approach is built around that requirement.
By combining specialized AI models, structured financial data and source-level auditability, the company is developing infrastructure designed to connect traditional financial research with the emerging world of AI agents and automated workflows.
As financial institutions continue adopting AI, reliable data infrastructure could become an increasingly important part of how these systems operate.
Editorial Note: Global SME Views selected Daloopa for this editorial spotlight based on its work at the intersection of artificial intelligence, financial data infrastructure, investment research and the emerging use of AI agents in financial services.
