About
Daloopa is a trusted AI financial data infrastructure platform designed to power the modern investment research workflow. It covers 5,300+ public companies globally, delivering 14 years of historical data with 4–10x more data points per company than competing providers—all with an average accuracy rate exceeding 99%. Every data point is hyperlinked to its original source, making outputs fully auditable and compliant with institutional standards. Daloopa serves over 160 of the world's largest hedge funds, mutual funds, and bulge bracket banks, and is used by leading AI companies including OpenAI and Anthropic. The platform offers multiple workflow solutions: **Daloopa Data Sheets** let analysts download full fundamental data sheets for any ticker and plug them directly into Excel; the **Excel Add-In** automates model updates during earnings season with one click; **Scout** is a native AI Excel agent that builds and maintains financial models using Daloopa's data layer; and the **API** allows developers to programmatically ingest KPIs at scale for custom analytics and AI agents. Daloopa also offers a **Model Context Protocol (MCP)** connector—recently expanded through a partnership with OpenAI—enabling LLMs and AI agents to query audit-ready financial data directly. This makes Daloopa the backbone of next-generation agentic investment research tools, cutting model initiation time by up to 70% and saving analysts an average of 2 hours per ticker during earnings updates.
Key Features
- Source-Linked, Audit-Ready Data: Every data point is hyperlinked to its original source document, delivering unparalleled transparency and >99% accuracy across millions of financial data points.
- Excel Add-In & Data Sheets: Automate model updates during earnings season with one click, or download full fundamental data sheets for 5,300+ global tickers to seamlessly initiate coverage in Excel.
- Scout AI Excel Agent: An AI-native Excel agent that agentically builds and maintains financial models using Daloopa's trusted data layer, delivering hallucination-free outputs for analysts.
- API & MCP Connector: Programmatically ingest thousands of KPIs per ticker at scale via REST API, or equip any LLM or AI agent with audit-ready financial data through Daloopa's Model Context Protocol connector.
- Broad Coverage & Deep History: Covers 5,300+ public companies globally with 14 years of data history and 4–10x more data points per company than other financial data providers.
Use Cases
- Buy-side analysts initiating coverage on new equities use Daloopa Data Sheets to cut model-building time by up to 70%, rapidly populating multi-year financial models with source-linked data.
- Investment research teams update earnings models during reporting season using the Daloopa Excel Add-In, saving an average of 2 hours per ticker with one-click data refreshes.
- Quantitative developers and data engineers use the Daloopa API to programmatically ingest thousands of fundamental KPIs for 5,300+ tickers into proprietary analytics platforms and screening tools.
- AI teams at financial institutions and technology companies integrate Daloopa's MCP connector to equip LLMs and agentic workflows with reliable, audit-ready financial data, eliminating hallucination in AI-generated financial analysis.
- Hedge funds and bulge bracket banks use Daloopa's data infrastructure as a standardized, institution-wide fundamental data layer to ensure consistency and accuracy across research, risk, and portfolio management workflows.
Pros
- Exceptional Data Accuracy: Daloopa's AI extraction pipeline achieves >99% accuracy with every data point sourced and hyperlinked, making it one of the most reliable financial datasets available.
- Significant Time Savings: Cuts up to 70% of the time spent building new models during coverage initiation and saves analysts an average of 2 hours per ticker during earnings season.
- AI & LLM Native: Purpose-built for the AI era with API and MCP support, allowing developers to power custom agents and LLMs with hallucination-free financial data.
- Institutional-Grade Trust: Used by 160+ top hedge funds, mutual funds, and bulge bracket banks, and trusted by leading AI companies like OpenAI and Anthropic.
Cons
- Primarily Enterprise-Focused: Daloopa's full feature set and pricing are geared toward institutional investors and financial professionals, which may be cost-prohibitive for individual retail investors or small teams.
- Limited to Public Equities: Coverage focuses on 5,300+ publicly listed global companies; private company data, fixed income, or alternative asset classes are not part of the core offering.
- Excel Dependency for Some Workflows: Several key workflows (Data Sheets, Add-In, Scout) are centered around Microsoft Excel, which may not suit teams operating in fully Python- or cloud-native environments.
Frequently Asked Questions
Daloopa is an AI-powered financial data platform that automates fundamental data extraction and delivery for 5,300+ public companies globally. It is designed for buy-side and sell-side analysts, financial institutions, and developers building AI agents or LLMs that require accurate, audit-ready financial data.
Daloopa achieves an average accuracy rate of over 99% across millions of data points. Every number is source-linked and hyperlinked to its original document, enabling full auditability and traceability.
Yes. Daloopa offers a dedicated Excel Add-In that lets analysts automate data updates in their models with a single click, as well as downloadable Data Sheets that can be added directly into any existing Excel model.
Daloopa's Model Context Protocol (MCP) connector allows LLMs and AI agents on any compatible AI platform to query Daloopa's audit-ready financial database directly. It enables trustable, hallucination-free financial outputs by giving AI systems access to source-linked fundamental data rather than unstructured web content.
Yes, Daloopa offers a free account option. Paid plans with expanded access to tickers, data history, API calls, and enterprise features are also available for professional and institutional users.