In the race to bring AI into institutional finance, accuracy has become the battleground—and Daloopa, a leader in fundamental financial data, just got a $13 million vote of confidence to lead the charge.
The New York-based platform, trusted by some of the world’s top equity research and investment teams, announced a strategic investment round led by Pavilion Capital. The funding will be used to meet rising demand for high-integrity, LLM-ready financial datasets—a critical gap as AI adoption explodes in hedge funds, banks, and asset managers.
This isn’t about faster data—it’s about trusted data that AI can understand, explain, and defend.
Model Context Protocol: The Infrastructure LLMs Were Missing
At the center of Daloopa’s expansion is its Model Context Protocol (MCP)—a connective tissue between structured, deeply sourced financial data and large language models like OpenAI’s GPT and Anthropic’s Claude.
With MCP, Daloopa gives LLMs access to:
- 10x more data points per public company than traditional vendors
- Verified sourcing down to filings, footnotes, and transcripts
- Cross-platform compatibility (Claude, OpenAI API, custom GPTs, etc.)
- Direct application into financial workflows like valuation, scenario modeling, and earnings comparisons
Already integrated into Claude for Financial Services, Daloopa’s MCP ensures LLMs don’t hallucinate numbers or make unverifiable claims—a recurring issue when AI tools rely on scraped or generalized internet data.
“We’re entering an era where AI is no longer optional in finance,” said Thomas Li, CEO of Daloopa. “But accuracy and auditability are non-negotiable. That’s where Daloopa leads.”
Why Financial AI Is Hitting a Wall Without Trusted Data
As Wall Street firms race to scale AI agents and LLM-powered workflows, they’re running into an inconvenient truth: general-purpose AI models often stumble in regulated, data-sensitive environments like finance.
- Public internet data lacks depth, structure, and source traceability
- Hallucinations—plausible-sounding but incorrect outputs—are rampant
- Manual cleanup and verification still drain analysts’ time
Daloopa’s data engine, which covers nearly 4,700 public companies globally, is engineered to solve exactly that. Every number links back to a source. Every source is ranked, stored, and retrievable. And every model using Daloopa via MCP can cite and verify—on command.
That alone puts Daloopa ahead of legacy data vendors who may offer breadth, but not LLM-native utility.
Practical Use Cases: From Hedge Funds to PE
MCP isn’t just a better API. It’s a workflow accelerator for AI-driven finance teams. Use cases already include:
- Hedge funds identifying delta shifts across earnings periods
- Private equity analysts generating comp sheets in seconds
- Valuation teams building models with built-in citation trails
- Strategy leaders deploying AI agents with full data lineage
- Equity analysts generating GPT reports with embedded traceability
These aren’t theoretical edge cases—they’re real deployments. And as AI agents mature from copilots to autonomous actors in financial research, Daloopa’s structured, sourced data becomes their foundation.
Competitive Landscape: Few Can Follow
While traditional vendors like Bloomberg, FactSet, and S&P have deep data reserves, they weren’t built for real-time AI interaction, contextual chaining, or memory recall. Daloopa is.
And unlike startups layering AI over generalized data, Daloopa starts with the raw material AI actually needs: verified, structured financial data with zero compromise on provenance.
By marrying that with LLM interoperability, Daloopa’s MCP may become to financial AI what REST was to web APIs—a standard, not a feature.
Looking Ahead: Building the Research Stack of the Future
With this new funding, Daloopa plans to:
- Expand its MCP protocol and deepen integrations with leading LLM platforms
- Accelerate R&D on AI agents for financial research
- Grow its coverage and tooling across global equities and private markets
It’s an ambitious roadmap, but a necessary one. As financial institutions push toward agentic research stacks, where AI models write memos, generate comps, and surface anomalies autonomously, only verifiable data will survive the scrutiny of auditors, regulators, and CIOs.
And in that world, Daloopa isn’t just another data vendor—it’s infrastructure.
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