How Artificial Intelligence Works in EDGARtag.AI

Combining Advanced AI with Specialized SEC Filing Expertise

EDGARtag.AI ™ is a fully automated, AI-powered web platform for Inline XBRL (iXBRL) tagging of SEC EDGAR filings. It combines powerful commercial AI engines with a proprietary AI agent developed by Advanced Computer Innovations, Inc. (ACI), which has been providing SEC filing software and services since 1993. This combination brings together the broad language understanding and reasoning capabilities of modern AI with specialized knowledge of SEC reporting requirements, XBRL taxonomies, and real-world financial filings.

The Proprietary EDGARtag.AI

™

Agent

At the center of the platform is the EDGARtag.AI ™ Agent, a proprietary software system that operates between the user's uploaded HTML filing and the back-end commercial AI engines. Rather than simply submitting a document to an AI model and accepting its output, the agent supervises and directs the entire tagging process. The agent is built on ACI's extensive experience developing SEC filing software and working with actual financial filings over more than three decades. It provides specialized instructions, establishes processing workflows, directs the AI engines, and coordinates tagging, validation, and error correction. This architecture allows general-purpose AI technology to be applied systematically to the specific technical and regulatory requirements of Inline XBRL.

Why a Specialized Agent Is Important

Commercial Large Language Models (LLMs) and reasoning AI engines can analyze financial statements, interpret disclosures, recognize accounting concepts, and generate structured information. However, these are general-purpose systems, and their knowledge of XBRL does not necessarily guarantee accurate or complete SEC-compliant tagging. The EDGARtag.AI ™ Agent addresses this limitation by providing detailed, task-specific guidance based on ACI's practical experience with SEC filings. It directs the AI engines in selecting appropriate taxonomy elements, applying reporting contexts, periods, units, and dimensions, and constructing valid Inline XBRL markup. This approach combines the flexibility and analytical capabilities of modern AI with the specialized procedures and technical discipline required for professional SEC filings. It also reduces dependence on the AI engines' general training alone.

Intelligent Financial Analysis and XBRL Tagging

Proper Inline XBRL tagging involves more than identifying numbers and assigning labels. Financial concepts must be interpreted in context, taking into account their accounting meaning, reporting periods, dimensional relationships, and placement within financial statements and disclosures. Under the direction of the EDGARtag.AI ™ Agent, the AI engines analyze the filing, identify financial concepts, select appropriate XBRL taxonomy elements, and generate the required Inline XBRL tags. The process addresses financial statements, footnotes, accounting policies, and other disclosures subject to SEC tagging requirements. The agent guides these operations to promote consistency, completeness, and compliance with applicable SEC and XBRL specifications.

The Automated Two-Pass Tagging and Correction Process

A central feature of EDGARtag.AI ™ is its AI-agent-supervised two-pass process, designed to produce complete and technically valid Inline XBRL filings. • First Pass — Analysis and Tagging: The EDGARtag.AI ™ Agent directs the commercial AI engines to analyze the uploaded HTML document, interpret its financial information and disclosures, identify appropriate taxonomy elements, and insert the required Inline XBRL tags. Specialized instructions guide the tagging of financial concepts, contexts, units, dimensions, and other technical components. • Second Pass — Validation and Automatic Correction: The system validates the initial tagging and identifies technical errors or inconsistencies. Under the agent's supervision, the AI engines examine the results and automatically correct identified problems, including issues involving taxonomy selections, contexts, dimensions, units, and Inline XBRL markup. This second pass distinguishes the process from a simple, one-step AI tagging operation. Instead of relying entirely on the initial AI-generated output, EDGARtag.AI ™ incorporates supervised validation and correction to improve tagging accuracy, completeness, and technical compliance. The objective is to produce a professionally tagged, complete, and validation-error-free filing that meets applicable SEC Inline XBRL requirements, without requiring users to perform manual XBRL tagging or technical corrections.

Automatic Handling of Filing Revisions

SEC filings frequently require revisions to financial figures, disclosures, or other information, sometimes shortly before submission deadlines. Traditionally, such changes can require manual modification of existing XBRL tags and additional validation. EDGARtag.AI ™ automates this process. Users upload a revised, untagged HTML filing, and the system transfers the existing XBRL tagging, updates it to reflect the changes, and performs validation and corrections. This eliminates the need to manually revise complex XBRL markup or repeat the entire tagging process from the beginning.

Simplifying SEC Filing Preparation

EDGARtag.AI ™ is designed for public companies, financial reporting professionals, and SEC filing agents, including users with no specialized XBRL knowledge or training. Users simply upload an HTML filing and download the completed iXBRL-tagged document after processing. The platform handles the technical work, including financial analysis, taxonomy selection, tagging, validation, and automatic error correction. The resulting document is ready for final review and SEC EDGAR submission. As with any XBRL filing process, the filer remains responsible for reviewing the completed tagging for accuracy and completeness.

Conclusion

EDGARtag.AI ™ combines the analytical power of commercial AI engines with ACI's specialized SEC filing expertise through its proprietary EDGARtag.AI ™ Agent. By supervising the AI engines and implementing a structured two-pass tagging, validation, and correction process, the system addresses limitations associated with using general-purpose AI models alone. The practical benefits include substantially reduced manual effort, improved tagging accuracy and consistency, more complete XBRL coverage, automatic error correction, faster processing, and lower filing preparation costs. This approach makes sophisticated, professional-grade Inline XBRL tagging accessible to SEC filers without requiring expensive specialized software or extensive technical expertise.

How Artificial Intelligence Works in

EDGARtag.AI

Combining Advanced AI with Specialized SEC

Filing Expertise

EDGARtag.AI ™ is a fully automated, AI-powered web platform for Inline XBRL (iXBRL) tagging of SEC EDGAR filings. It combines powerful commercial AI engines with a proprietary AI agent developed by Advanced Computer Innovations, Inc. (ACI), which has been providing SEC filing software and services since 1993. This combination brings together the broad language understanding and reasoning capabilities of modern AI with specialized knowledge of SEC reporting requirements, XBRL taxonomies, and real-world financial filings.

The Proprietary EDGARtag.AI

™

Agent

At the center of the platform is the EDGARtag.AI ™ Agent, a proprietary software system that operates between the user's uploaded HTML filing and the back-end commercial AI engines. Rather than simply submitting a document to an AI model and accepting its output, the agent supervises and directs the entire tagging process. The agent is built on ACI's extensive experience developing SEC filing software and working with actual financial filings over more than three decades. It provides specialized instructions, establishes processing workflows, directs the AI engines, and coordinates tagging, validation, and error correction. This architecture allows general-purpose AI technology to be applied systematically to the specific technical and regulatory requirements of Inline XBRL.

Why a Specialized Agent Is Important

Commercial Large Language Models (LLMs) and reasoning AI engines can analyze financial statements, interpret disclosures, recognize accounting concepts, and generate structured information. However, these are general- purpose systems, and their knowledge of XBRL does not necessarily guarantee accurate or complete SEC-compliant tagging. The EDGARtag.AI ™ Agent addresses this limitation by providing detailed, task-specific guidance based on ACI's practical experience with SEC filings. It directs the AI engines in selecting appropriate taxonomy elements, applying reporting contexts, periods, units, and dimensions, and constructing valid Inline XBRL markup. This approach combines the flexibility and analytical capabilities of modern AI with the specialized procedures and technical discipline required for professional SEC filings. It also reduces dependence on the AI engines' general training alone.

Intelligent Financial Analysis and XBRL Tagging

Proper Inline XBRL tagging involves more than identifying numbers and assigning labels. Financial concepts must be interpreted in context, taking into account their accounting meaning, reporting periods, dimensional relationships, and placement within financial statements and disclosures. Under the direction of the EDGARtag.AI ™ Agent, the AI engines analyze the filing, identify financial concepts, select appropriate XBRL taxonomy elements, and generate the required Inline XBRL tags. The process addresses financial statements, footnotes, accounting policies, and other disclosures subject to SEC tagging requirements. The agent guides these operations to promote consistency, completeness, and compliance with applicable SEC and XBRL specifications.

The Automated Two-Pass Tagging and Correction

Process

A central feature of EDGARtag.AI ™ is its AI-agent- supervised two-pass process, designed to produce complete and technically valid Inline XBRL filings. • First Pass — Analysis and Tagging: The EDGARtag.AI ™ Agent directs the commercial AI engines to analyze the uploaded HTML document, interpret its financial information and disclosures, identify appropriate taxonomy elements, and insert the required Inline XBRL tags. Specialized instructions guide the tagging of financial concepts, contexts, units, dimensions, and other technical components. • Second Pass — Validation and Automatic Correction: The system validates the initial tagging and identifies technical errors or inconsistencies. Under the agent's supervision, the AI engines examine the results and automatically correct identified problems, including issues involving taxonomy selections, contexts, dimensions, units, and Inline XBRL markup. This second pass distinguishes the process from a simple, one-step AI tagging operation. Instead of relying entirely on the initial AI-generated output, EDGARtag.AI ™ incorporates supervised validation and correction to improve tagging accuracy, completeness, and technical compliance. The objective is to produce a professionally tagged, complete, and validation-error-free filing that meets applicable SEC Inline XBRL requirements, without requiring users to perform manual XBRL tagging or technical corrections.

Automatic Handling of Filing Revisions

SEC filings frequently require revisions to financial figures, disclosures, or other information, sometimes shortly before submission deadlines. Traditionally, such changes can require manual modification of existing XBRL tags and additional validation. EDGARtag.AI ™ automates this process. Users upload a revised, untagged HTML filing, and the system transfers the existing XBRL tagging, updates it to reflect the changes, and performs validation and corrections. This eliminates the need to manually revise complex XBRL markup or repeat the entire tagging process from the beginning.

Simplifying SEC Filing Preparation

EDGARtag.AI ™ is designed for public companies, financial reporting professionals, and SEC filing agents, including users with no specialized XBRL knowledge or training. Users simply upload an HTML filing and download the completed iXBRL-tagged document after processing. The platform handles the technical work, including financial analysis, taxonomy selection, tagging, validation, and automatic error correction. The resulting document is ready for final review and SEC EDGAR submission. As with any XBRL filing process, the filer remains responsible for reviewing the completed tagging for accuracy and completeness.

Conclusion

EDGARtag.AI ™ combines the analytical power of commercial AI engines with ACI's specialized SEC filing expertise through its proprietary EDGARtag.AI ™ Agent. By supervising the AI engines and implementing a structured two-pass tagging, validation, and correction process, the system addresses limitations associated with using general-purpose AI models alone. The practical benefits include substantially reduced manual effort, improved tagging accuracy and consistency, more complete XBRL coverage, automatic error correction, faster processing, and lower filing preparation costs. This approach makes sophisticated, professional-grade Inline XBRL tagging accessible to SEC filers without requiring expensive specialized software or extensive technical expertise.