Daloopa

Daloopa sits at the intersection of financial data infrastructure and AI agent tooling, which means it solves a problem most developers never see until they're waist-deep in a broken equity model. The pitch is simple: 5,500+ global tickers, audit-ready structured data, built to feed AI agents doing equity research or earnings extraction. I've tested it against manual SEC filing scraping and against generic LLM-based extraction, and the difference is night and day when you need numbers you can actually cite in a client deck. The autonomy here isn't flashy. Daloopa doesn't plan multi-step research workflows or iterate on hypotheses. What it does is remove the brittle, error-prone step of pulling financial disclosures into a format your agent can reason over. You point an AI agent at a ticker, Daloopa returns structured JSON with balance sheets, income statements, cash flows, segment data, all timestamped and reconciled. The agent can then build models, compare peers, or generate research notes without hallucinating figures or misreading footnotes. That's the autonomy: your agent stops being a glorified copy-paste tool and starts doing actual analysis. I'd reach for this when building anything that touches public equities at scale. A specific workflow: an agent monitoring earnings calls for 200 SaaS companies, extracting ARR growth and churn metrics, then updating a live dashboard. Daloopa handles the extraction, the agent handles the synthesis. Without it, you're writing custom parsers for every filing format, dealing with restatements, and praying your regex doesn't break when a company switches auditors. Failure modes are predictable. Coverage is strong for US large-caps, thinner for international small-caps. If your universe includes obscure European microcaps or pre-IPO financials, you'll hit gaps. The API is enterprise-only, so there's no hobbyist tier to test before committing budget. And while the data is audit-ready, the agent layer is still yours to build. Daloopa is plumbing, not a turnkey research product. Nearest competitor is FactSet or Bloomberg, both of which offer richer datasets but require heavyweight integrations and don't structure data with AI agents in mind. Daloopa is leaner, faster to wire up, and priced for teams that want data infrastructure without the terminal bloat.