Daloopa is a specialized AI-powered data infrastructure platform designed for financial analysts, investment firms, and developers building financial AI agents. The platform focuses on extracting, structuring, and delivering highly accurate financial data from thousands of global tickers. By converting unstructured financial documents, such as public filings and earnings reports, into clean, structured data feeds, Daloopa helps investment teams bypass manual data entry and accelerate their research workflows. The platform is engineered to integrate seamlessly with existing financial models, spreadsheets, and modern large language models, providing a reliable foundation for automated analysis and decision-making. Through its robust data pipelines, Daloopa ensures that financial institutions and AI developers can access historical and real-time financial metrics with high precision. Ultimately, the service serves as a critical bridge between raw corporate disclosures and the sophisticated quantitative models used by modern financial services.
Problem: Financial analysts spend days manually extracting historical KPIs and financial metrics from PDF filings to build new valuation models.
Solution: Daloopa provides downloadable, pre-populated Data Sheets with up to 14 years of historical data across 5,500+ tickers.
Example: An equity research analyst uses Daloopa Data Sheets to instantly generate a comprehensive Excel template for a newly covered tech firm, cutting initial modeling time.
Problem: Updating existing financial models with fresh quarterly data during earnings season is time-consuming and prone to manual entry errors.
Solution: The Daloopa Excel Add-In allows analysts to refresh their active models with the latest reported figures in one click.
Example: A hedge fund analyst uses the Excel Add-In to update their portfolio models with newly released Q3 earnings numbers instantly, saving hours per ticker.
Problem: General-purpose LLMs and AI agents often hallucinate financial data or struggle to retrieve precise, unstructured table metrics from public filings.
Solution: Developers integrate Daloopa's API or MCP (Model Context Protocol) to supply AI agents with a clean, structured, and auditable financial database.
Example: A fintech developer builds an automated investment chatbot that uses Daloopa's API to retrieve source-linked, verified revenue KPIs for 5,500+ public companies.
Target audience: Best for: Buy-side and sell-side financial analysts, investment software developers, and hedge fund portfolio managers.
Pricing: Paid · Categories: Developer Tools, Finance, Research
Tags: ai agent, developer tools, finance, research, spreadsheet