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AddedFeb 15, 2024
UpdatedJun 1, 2026
Daloopa

Daloopa

Series C

Daloopa provides AI-driven financial data infrastructure powering investment research workflows for global asset managers and AI platforms.

HQ
New York City, NY, US
Founded
2019
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Contents

  1. 01Executive Summary
  2. 02Products & Services
  3. 03Market Outlook
  4. 04Competitive Strengths
  1. 01Executive Summary
  2. 02Products & Services
  3. 03Market Outlook
  4. 04Competitive Strengths

Memo

Daloopa raised a $47 million Series C led by Brighton Park Capital in May 2026, with participation from Squarepoint Capital, Touring Capital, and Nexus Venture Partners.

The capital expands engineering, product, and go-to-market teams and accelerates adoption of Daloopa's structured data layer across AI investment research, where the company now serves 160+ hedge funds, mutual funds, bulge bracket banks, and leading AI platforms.

Product Overview

Daloopa is the financial data layer for investment research, delivering source-linked fundamental data across 5,500+ public companies with 14 years of history.

Its product suite spans Daloopa Data Sheets, the Excel Add-In, the Scout AI Excel agent, the Daloopa API, the MCP connector set, and Daloopa Cloud for Snowflake, Databricks, and AWS S3 delivery.

Market Outlook

Investment firms are moving from AI experimentation to production workflows, where accuracy and reliability are non-negotiable for valuation, earnings analysis, and portfolio modeling.

Daloopa frames the bottleneck in AI-driven finance as data infrastructure rather than model intelligence, citing its published benchmark in which grounded structured data improved AI agent accuracy by up to 71 percentage points over web retrieval.

Competitive Advantages

Daloopa's dataset covers 5,500+ public companies globally and delivers up to 10x more data points per company than other providers, with every datapoint hyperlinked to its original source for auditability.

The MCP and Partner API extend that data layer into ChatGPT, Claude, Perplexity, Rogo, and major data clouds (Snowflake, Databricks, AWS S3), giving AI agents the structured inputs they need for valuation, earnings, and portfolio modeling.