By DaloopaLearning Objectives
- Understand how Daloopa extracts and structures company fundamentals from financial filings
- Learn why grounded, auditable data matters for AI accuracy in finance
- Recognize Daloopa's role as a "picks-and-shovels" data layer for AI-driven finance
What Is Daloopa?
Daloopa is a financial-data platform that uses specialized AI models to read company filings, earnings presentations, and investor materials and turn them into structured, analyst-ready fundamentals. For thousands of public companies, it captures line items such as revenue segments, margins, and operating metrics, then delivers them with source hyperlinks that let an analyst click any number and trace it back to the exact filing it came from. The problem it solves is old and expensive: equity analysts spend enormous time manually copying figures from documents into spreadsheet models, and every hand-keyed number is a chance for error. Daloopa automates that data-gathering step and keeps a model updated as new filings arrive.
Daloopa was founded in 2017 and is headquartered in New York, started by former buy-side analysts who lived the manual-modeling pain firsthand. In May 2026 the company raised a $47 million Series C led by Brighton Park Capital, with participation from investors including Squarepoint Capital, Touring Capital, and Nexus Venture Partners. The company positions itself as the reliable data layer behind AI-driven finance, and it is an Anthropic finance-agents data partner — its structured data can be delivered directly into AI tools including Claude, so agents answer questions grounded in audited numbers rather than scraped web pages.
💡Key Concept
Structured data as the foundation for AI finance: Large language models are only as reliable as the numbers they reason over. When an AI agent pulls figures from web pages, it can grab a wrong fiscal period, a mislabeled metric, or a stale value — errors that quietly distort a valuation. Daloopa's argument is that the real constraint in AI finance is not model intelligence but data quality, so it supplies clean, consistent, source-linked fundamentals that give agents a trustworthy foundation.
✅Tip
Visit Daloopa: daloopa.com — for equity analysts, asset managers, and fintech developers; enterprise, pricing by quote.
Core Capabilities
Fundamentals Extraction
Daloopa's specialized models parse filings, earnings decks, and press releases to extract detailed financials — including granular segment and operating metrics that generic data feeds often skip. Each captured value is tied to its source document, so users can verify provenance instead of trusting a black box.
Excel Modeling Copilot
The platform plugs into Excel, letting analysts refresh their models with updated figures rather than re-keying numbers each quarter. As new filings publish, the underlying data updates, which shortens the scramble around earnings season and reduces copy-paste mistakes.
Data Layer for AI Agents
Beyond the spreadsheet, Daloopa exposes its data through programmatic access and connectors, including Model Context Protocol (MCP) integration with AI assistants. This lets developers and agentic finance workflows query structured fundamentals directly, so an AI agent's answers rest on auditable data.
Strengths
- Source-linked auditability — every extracted number links back to the original filing, so analysts and compliance teams can verify figures rather than trust them blindly.
- Depth of coverage — Daloopa captures granular segment and operating metrics that many standard data providers leave out, which is exactly what detailed models need.
- AI-ready delivery — programmatic access and MCP connectors make the same clean data usable by both human analysts and AI agents.
- Time savings at earnings — automated updates cut the manual re-keying that consumes analyst hours each reporting cycle.
Limitations & Considerations
- Extraction accuracy is everything — in financial modeling, a single wrong figure can cascade through a valuation, so any extraction error carries real weight even when overall accuracy is high.
- Analysts still audit the numbers — Daloopa reduces manual data entry but does not remove the analyst's responsibility to sanity-check outputs; the source links exist precisely because human review remains essential.
- Enterprise focus — the product is aimed at professional investors and firms, with pricing by quote rather than a self-serve consumer tier.
- Coverage boundaries — capture is strongest for reported public-company fundamentals; forward-looking judgment, private-company data, and unconventional metrics may still require manual work.
Best Use Cases
| Task | Why Daloopa |
|---|---|
| Updating an equity model at earnings | Auto-refreshes fundamentals with source links instead of manual re-keying |
| Building a detailed segment model | Captures granular operating metrics generic feeds omit |
| Grounding an AI finance agent | Supplies clean, auditable data via API and MCP so agents answer from real numbers |
| Compliance and audit review | Every figure traces back to its original filing for verification |
Getting Started
- Request a demo through the Daloopa website and identify the companies and metrics your team models most.
- Connect the Excel add-in and load a sample model to see fundamentals populate with source links.
- For AI workflows, set up programmatic or MCP access so agents can query structured data directly.
- Establish a review step so analysts verify key figures against the linked sources before relying on them.
Key Takeaways
- Daloopa uses specialized AI to extract and structure company fundamentals into analyst-ready, source-linked data.
- Its thesis is that data quality — not model intelligence — is the true constraint in AI-driven finance.
- It serves both humans (an Excel modeling copilot) and machines (a data layer for agentic workflows), and is an Anthropic finance-agents data partner.
- Extraction accuracy matters enormously in modeling, and analysts still bear responsibility for auditing the numbers.
