Daloopa: IDP Software Vendor

AI-powered financial data infrastructure that automates fundamental data extraction from SEC filings for equity analysts and financial institutions.

Overview

Daloopa is an AI-powered financial data infrastructure company founded in 2019 by former Point72 analyst Thomas Li alongside Jeremy Huang (ex-Airbnb/Meta) and Daniel Chen (ex-Microsoft). The New York-based company has raised over $50 million across multiple rounds, including an $18 million Series B led by Touring Capital with Morgan Stanley participation in August 2025 and a $13 million strategic investment from Pavilion Capital in late 2025.

The platform serves over 150 major financial institutions - hedge funds, private equity firms, and investment banks - covering 5,000+ public companies globally with claimed accuracy exceeding 99% and full source attribution to regulatory filings. Daloopa delivers up to 10x more data points per company than unnamed competing providers, though no independent comparison has verified that figure.

Daloopa's public positioning has shifted from financial data provider toward agentic finance infrastructure. In December 2025, the company launched its Model Context Protocol (MCP) with connectors for both Anthropic's Claude and OpenAI's ChatGPT, treating MCP as its primary go-to-market channel for AI-native buyers rather than building a standalone interface. A Slashdot directory listing confirms Daloopa is named alongside S&P Global as a core data source within Anthropic's Claude for Financial Services - placing it at the institutional data tier - though no primary Anthropic product page has confirmed partnership terms or launch date.

In February 2026, Daloopa published the FinRetrieval benchmark: 500 real-world financial questions tested across three frontier agent systems (OpenAI Agents SDK with GPT-5.2, Anthropic Agent SDK with Claude Opus 4.5, Google ADK with Gemini 3 Pro). All three reached approximately 90% accuracy on Daloopa's structured database versus roughly 19-20% from public web sources - a gap of up to 71 percentage points, confirmed by independent coverage. The benchmark is vendor-commissioned and has not been independently replicated; treat figures as vendor-reported. CEO Thomas Li framed the finding directly: "Accuracy in AI-driven finance isn't just a model problem, it's a data access problem."

The benchmark's secondary finding carries more competitive weight than the headline number: closing the remaining gap from 90% to 99%+ is blocked by fiscal calendar misalignment for non-US companies and entity naming inconsistencies - data infrastructure failures, not model failures. By naming these problems publicly, Daloopa is staking a claim on the solution space and signaling that raw model improvements from Anthropic or OpenAI won't close the gap without better underlying data.

How Daloopa Processes Documents

Daloopa's extraction pipeline targets SEC filings, earnings transcripts, and supplemental disclosures, normalizing outputs into structured datasets with every data point hyperlinked to its source filing for auditability. The core products operate as a layered stack:

• Excel Add-in: One-click model updates with hyperlinked source verification inside existing spreadsheets. The Retrofitter links existing Excel models to source documents and audits historical inputs. The Updater automatically refreshes models with latest disclosures while maintaining native formatting, with a claimed error rate below 1%.
• Scout: AI-powered model-building tool that generates complete financial models from natural language prompts with transparent reasoning. A vendor-published workflow demonstrates Scout's MCP integration inside Claude retrieving six years of standardized depreciation data across Microsoft, Alphabet, Amazon, Meta, and Oracle in a single conversation - generating an interactive dashboard with seven chart views and five data tables. The vendor claims five-minute completion; no third party has verified this.
• API Platform: 8 endpoints with JSON/CSV output, webhook notifications, and keyword search. Model strings, plan tiers, and GA/beta status for the MCP integration are not publicly disclosed.
• Model Context Protocol (MCP): Standardized client-server architecture replacing custom AI integrations, with containerized deployment support and connectors for both Claude and ChatGPT-based workflows.

The FinRetrieval benchmark identified two infrastructure gaps that cap accuracy below 99%+: non-December fiscal year-ends for non-US companies cause calendar misalignment, and inconsistent entity naming conventions or tickers cause retrieval failures. These are the stated next product frontiers.

Use Cases

Equity Research and Financial Modeling

Analysts save an average of 2 hours per ticker each earnings season, with a 70% reduction in time for new model builds. The platform enables simultaneous updates across all coverage models rather than forcing prioritization of select companies - a structural advantage during earnings season when multiple companies report within days of each other.

The FinRetrieval benchmark demonstrated the agentic use case concretely: hedge funds can use MCP-connected agents to identify quarter-over-quarter inflections, run scenario simulations, and generate equity research with full source traceability - at approximately 90% accuracy on structured Daloopa data versus roughly 19-20% on public web sources. Which of the three tested agent frameworks achieved the full 71-point gain versus smaller gains is not specified in the published results.

Due Diligence and Private Markets Research

Private equity and hedge funds access 13 years of historical data including company guidance, KPIs, segment breakdowns, and geographic data for comparable company analysis. The 10x data density claim - more data points per company than unnamed competing providers - is vendor-reported without independent verification. Alkymi addresses a comparable need in financial services, focusing on extracting and transforming unstructured documents into standardized datasets for investment workflows. Cognaize takes a neuro-symbolic approach to the same financial document extraction problem, targeting structured data extraction from complex filings for institutional clients.

Compliance and Audit

Every data point is hyperlinked to its original SEC filing or earnings transcript, enabling compliance teams to trace extractions to source for regulatory requirements. CEO Li has emphasized that "accuracy and auditability are non-negotiable" in finance AI - a positioning that distinguishes Daloopa from general-purpose LLM tools that generate outputs without source grounding. Acuity Knowledge Partners takes a services-led approach to the same auditability requirement, combining AI-powered document processing with human analyst oversight for 800+ financial institutions. For teams evaluating agentic document processing more broadly, the MCP architecture Daloopa has adopted represents one implementation pattern for connecting structured financial data to autonomous agent workflows.

Technical Specifications

Resources

• Daloopa Homepage
• Daloopa Products
• Daloopa API
• Model Context Protocol Guide
• FinRetrieval Benchmark Report (PR Newswire)
• Scout MCP Workflow Demonstration
• TechCrunch: Daloopa trains AI to automate financial analysts' workflows
• completeaitraining.com: Benchmark coverage

Company Information

Headquarters: New York City, NY Website: https://daloopa.com/ Founded: 2019 Employees: ~300