Daloopa

Daloopa operates an AI-driven financial data platform that extracts fundamental data and key performance indicators from company filings and disclosures and delivers them as structured, source-linked datasets. Its coverage spans more than 6,000 public company tickers globally with roughly 14 years of history, and the company states it provides 4-10x more data points per company than other providers, with an average accuracy rate above 99% across millions of data points. Each data point is hyperlinked back to its original source document, which the company positions as an audit trail for research outputs.

The product suite addresses several delivery formats: downloadable Daloopa Data Sheets for initiating coverage on a ticker; an Excel Add-In that refreshes existing models against Daloopa's database during earnings season; Scout, an AI Excel agent that builds and maintains financial models on top of Daloopa's data layer; a programmatic API for ingesting KPIs at scale into custom analytics and AI agents; an MCP interface for connecting LLMs and AI agents to the dataset; and cloud-native data delivery into customer data workspaces. The company also publishes a "FinRetrieval" benchmark comparing how LLM-powered agent systems perform on financial retrieval tasks when using an auditable financial database versus the public web.

Daloopa frames its users in three groups: analysts building and updating models, builders creating financial agents that require an audit-ready data layer, and partners embedding the data into agentic workflows to reduce hallucination.

Business model

Daloopa sells access to its fundamental financial dataset and associated tooling to financial institutions and to AI companies, delivered through spreadsheet add-ins, downloadable data sheets, an API, an MCP server and cloud data integrations. A free account tier is offered alongside a demo request path for institutional buyers.

Traction

The website reports adoption by 185+ of the largest hedge funds, mutual funds and bulge bracket banks and by five AI companies (Anthropic, OpenAI, Google, Perplexity and Microsoft). Claimed customer outcomes include a 70% reduction in time to build new models when initiating coverage and an average of two hours saved per ticker when updating models during earnings season.