Daloopa is a financial data platform that provides fundamental data for the investment research field, serving large hedge funds, mutual funds, and large banks, and addressing analysts' needs during the 13-week investment research cycle.
In terms of use, Daloopa mainly solves the problem of the time-consuming data collection and organization that investment analysts face when building and updating financial models. According to its official statements, when initiating coverage on a company, Daloopa can reduce the time spent building a new model by 70%; when updating models during earnings season, it can save an average of 2 hours per stock.
Its main capabilities include: the platform covers more than 6,000 publicly listed companies worldwide, provides 14 years of data history, and has 4 to 10 times the number of data points per company compared with other vendors. Every data point is hyperlinked to its original source, and the output is auditable; the company says its average accuracy across millions of data points exceeds 99%. The platform supports multiple workflows with a single data layer.
In terms of the product suite, the official description includes: complete data tables downloadable for more than 6,000 global tickers, used to quickly launch coverage or add to existing Excel models; automatic data updates in Excel by linking to Daloopa's comprehensive fundamentals database; Scout is an AI Excel agent embedded with Daloopa's native data layer, providing analysts with sourced, hallucination-free output covering more than 6,000 stocks; complete fundamental datasets can be ingested programmatically at scale, pulling thousands of KPIs directly into projects, with every data point linked to its original source for audit; Daloopa data infrastructure is provided for LLMs and AI agents so they can produce trustworthy output based on auditable financial data.
In terms of how to use it, the official information mentions adding data tables to existing Excel models, linking the database in Excel for automatic updates, programmatic access via API, and direct access to data within the workspace. For AI agents, the company presented a financial retrieval test called FinRetrieval, comparing the performance of LLM agent systems when using an auditable financial database versus public web data.
Applicable scenarios include investment analysis, financial modeling, initial coverage research, earnings season model updates, and providing auditable financial data for AI agents.
In terms of limitations, the official materials do not provide specific information such as pricing, free trials, full platform-supported regions, or specific data update frequency; these details are not currently provided.
