Below is a sample of tools and services from XBRL US Members. Click the title of the tool or service to read details contributed by our members.

Need more options? Choose one of the product or service feature links at right to see which tools and services correspond to a specific feature, or use the browse all link below or catalog link at right to see the full list.

Tool/Service

eFINForms

The eFINForms is a web-based application and user-friendly enterprise system that does not require the end user to interact with XBRL or Tagging to the Taxonomy. Our software will contain your previous years Form Submissions provided by FERC, which can be rolled over to create the next Form Filing with ease. It will pre-populate previous […more]

browse products & services with similar features: ToolFor FERC Filersweb-based training24/7 technical supportinline XBRLversion controllocal rule checkingSystrends


Tool/Service

XBRL US Large Language Model Connector

Add XBRL data from the XBRL US Public Filings Database to your artificial intelligence tasks. This tool is a Model Context Protocol server (MCP) and it works best with Anthropic’s Claude LLM (Opus 4.6 or later). Learn more about getting started and results with XBRL and LLMs. As a demo, the volume of data retrieved […more]

browse products & services with similar features: Analystexternal reportingUS SECIssuerUS CongressDeveloperdata reportingfinancial information analysisdata analysisXBRL US


Tool/Service

TagniFi Datasets

Timely and accurate standardized balance sheet, income statement and cash flow statement data. Standardized footnote data including inventory, PP&E, leases, pensions, stock options, segments, and more. End-of-day price and volume data. Available as split-adjusted and dividend-adjusted for your analysis. Learn about these and other datasets changing how people use financial data.

browse products & services with similar features: data reportingfinancial information analysisdata analysisTagniFi


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AI Meets Auditing: Build Smarter Accounting Estimates with Multi-Agent LLMs
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