Financial statements
were never meant to be compared.
We fixed that.

You can look at two companies side by side — but you're comparing labels, not concepts. Apple reports "Net sales," Microsoft reports "Revenue," and Walmart reports "Net revenues." These all mean the same thing, but raw data treats them as different line items. Multiply that across every row of three financial statements for 14,000 entities and the problem becomes obvious: raw, as-reported data was never designed for comparison. FinEL's classification engine solves this by mapping every filing to a single, consistent schema before you ever see it.

It reads every 10-K and 10-Q filed with the SEC and maps each reported line item — regardless of what the company called it — to a standardized financial structure. This isn't simple renaming. The engine handles custom XBRL extensions, non-standard groupings, and company-specific reporting quirks. After classification, every statement is validated against GAAP accounting logic: do assets equal liabilities plus equity? Does net income flow correctly from the income statement to cash flows? If something doesn't balance, it gets flagged — not shipped. The result is 21 million data points you can trust to be genuinely comparable.

The core capability is the same — and we're upfront about that. Bloomberg, Capital IQ, and FactSet all employ teams to normalize financial data. The difference isn't quality; it's who gets access. Those platforms cost $20,000–$50,000 per year and are designed for institutional trading floors. FinEL was built from scratch for a different audience: independent investors, analysts, content creators, and small teams who need the same analytical foundation without the institutional price tag or workflow complexity. We're not a cheaper terminal — we're normalized financial data as its own product, built for how individuals actually work.

Directly from SEC EDGAR — the same primary source Bloomberg uses. Every 10-K and 10-Q for 14,000+ publicly traded U.S. entities is ingested, parsed, and classified using our proprietary models. We don't license data from third-party aggregators or repackage someone else's feed. The entire dataset is built in-house, from the filing to the finished data point.

Yes — that's the whole point of normalization. Because every company's financials are mapped to the same schema, you can compare Apple's operating margin to Walmart's, or benchmark NVIDIA's capital efficiency against Pfizer's. Some metrics are naturally more meaningful within an industry, but the standardized structure makes cross-industry analysis genuinely possible. Industry averages provide additional context so you can see both the absolute comparison and how each company performs relative to its own peers.

Ask questions in natural language and get answers grounded in normalized financial data — not generic web scraping. "Compare AAPL and MSFT gross margins over 5 years" returns an actual chart built from classified data. "Which semiconductor company has the highest ROIC?" queries the real dataset, not a language model's training data. Because the underlying data is normalized, the AI can make cross-company comparisons that would be unreliable with raw, as-reported numbers.

Both. Pro subscribers get a REST API for programmatic access to normalized financial data — line items, ratios, financial statements, and screener queries. Pro subscribers also get access to our MCP (Model Context Protocol) server, which lets you connect FinEL's data directly to AI tools like Claude, Cursor, or any MCP-compatible client. Ask your AI to pull comps, compare margins, or analyze filings — powered by the same classified data that runs the platform, not raw web scraping.

Financial data is updated as new filings are processed. When a company files a 10-K or 10-Q with the SEC, our pipeline ingests, classifies, validates, and publishes the normalized data. Market data (prices, indices, sector performance) updates throughout the trading day. Fund holdings from SEC NPORT-P filings are updated quarterly as they become available.