Financial Datasets

Investors and AI teams building agents need accurate, auditable financial data to make decisions with no margin for error. Today they must manually read SEC filings, earnings releases, and press releases to extract KPIs, guidance, and non-GAAP metrics, a slow process that doesn't scale across a full universe of stocks. Inconsistent field names and formats across companies force costly remapping work when building automated systems. Without citations tied to source documents, it's hard to verify that an agent's numbers are actually correct before acting on them.

Financial Datasets offers an API covering 27,530 stocks with over 30 years of fundamentals and KPIs, standardized with consistent field names across every company. Every data point, from operational KPIs and forward guidance to non-GAAP reconciliations, income statements, balance sheets, cash flow, insider trades, and segment breakdowns, is cited back to its source such as an SEC accession number, filing excerpt, or press release quote. The API also provides historical point-in-time financials so backtests reflect what the market actually saw each year, and supports searching years of 10-K sections at machine speed. It reports published benchmarks for accuracy, latency, coverage, and freshness, each with stated sample size and methodology (1,000 companies across 75 sectors, 20,000 data points verified per audit cycle).