Regulatory filings are the atomic unit of financial data. Don't limit your view to the SEC. Access a unified feed of 10-Ks, 10-Qs, and 8-Ks alongside their global equivalents—UK Annual Reports, EU Interim Statements, and APAC Ad-Hoc Disclosures. All parsed, cleaned, and standardized into JSON.
One Schema. Every Market. We solve the "Babel Fish" problem of global compliance. A "10-K" in New York is an "Annual Report" in London and a "Yuho Report" in Tokyo. We map them all to a single standard.
Stop writing custom scrapers for every regulator. We ingest data from EDGAR (US), SEDAR (Canada), ESMA (EU), and FSA (Japan) and map disparate document types to a unified taxonomy. Request type=annual_report and get the 10-K for Apple alongside the Annual Report for Toyota.
Don't process the whole haystack. Our parser identifies and extracts specific high-value sections like Risk Factors (Item 1A), MD&A (Item 7), and Notes to Financials. Feed just the relevant text chunks into your NLP models to detect shifting risk sentiment.
Raw XBRL XML is a nightmare to parse. We do the heavy lifting, converting complex XBRL tags into flat, machine-readable JSON objects. Get the exact "As-Reported" values for revenue, assets, and liabilities without wrestling with XML namespaces.
"Get me the 'Risk Factors' section from the latest Tesla 10-K."
Download the full history of global annual reports to train a multi-lingual financial LLM.
Query the corpus: "Show me all UK companies disclosing Brexit-related supply chain risks."
Direct links to the original Source PDF/HTML for human audit.
{
"ticker": "VOD.L",
"exchange": "LSE",
"filing_type": "Annual_Report",
"mapped_type": "10-K_Equivalent",
"period_end": "2024-03-31",
"filing_date": "2024-05-21",
"source_url": "https://filings.finiq.data/lse/vod/2024_annual.pdf",
"extracted_sections": {
"risk_factors": {
"text": "The Group faces regulatory risks in the German market...",
"word_count": 4500,
"sentiment_score": -0.45
},
"management_discussion": {
"text": "Revenue growth was driven by service revenue in Africa...",
"sentiment_score": 0.12
}
}
} mapped_value : The magic field. We tag international documents with their US equivalent (e.g., 10-K_Equivalent, 8-K_Equivalent). This allows you to build a global screening tool without needing to know the specific regulatory code for "Material Event" in Germany. extracted_sections : We use NLP to identify the start and end of critical chapters. This lets you ignore the boilerplate "Table of Contents" and focus purely on the Risk Factors or MD&A. sentiment_score : We pre-calculate sentiment for each specific section. A sharp drop in sentiment specifically within the "Risk Factors" section is often a leading indicator of credit downgrades.
Our team of experienced financial advisors is here to provide personalized guidance and support.
For US SEC filings, our latency is <2 seconds from EDGAR publication to API availability. For global markets, latency varies by exchange but is typically within 1-5 minutes of public release.
Yes. For major markets (Japan, China, Brazil), we provide the Original Raw Text alongside an English Translation (via Neural Machine Translation). You can choose which version to consume via the API.
Yes. Our Full-Text Search API allows you to query the entire 25-year corpus. Example: query="supply chain" AND ticker="AAPL" AND section="risk_factors".
We link them. The API returns the original filing and flags any subsequent amendments (is_amended: true). You can request the "Amended Only" diff to see exactly what changed (often a red flag for accounting irregularities).
Yes. Our "Cross-Document Query" engine allows you to search a theme (e.g., "AI CapEx") across 10-Ks, Earnings Calls, and Expert Transcripts simultaneously, giving you a complete view of the topic.
We cover 60+ global markets. Our platform includes built-in translation for over 60 languages , allowing you to search and read filings or transcripts from local markets in English without losing context.
