Pacioli.ai Financial Disclosure Automation Platform

Pacioli.ai is a comprehensive regulatory disclosure automation infrastructure that enables professionals, regulators, and standard setters to design, create, and validate disclosure infrastructure for any reporting framework without requiring structured data expertise. The platform is the first implementation of the Standard Business Report Model (SBRM) specification adopted by the Object Management Group (OMG).

Financial Reporting Automation

Model-Based Assembly: Pacioli.ai enables the assembly of knowledge and logic-based financial reporting models that are reused for automating financial disclosure every reporting period. Model-based assembly automatically picks terms and definitions from USGAAP, IFRS, and most other reporting scheme taxonomies, constraining financial report logic, data, and structure.

Automated Tagging and Validation: Tagging is automated and accurate, eliminating inconsistencies that are common in most structured disclosure development. The Logic and Rules Engine provides automated validation and compliance checking, ensuring accuracy and consistency across all disclosure documents.

Machine-Readable Outputs: The Luca Suite enables creation of machine-readable financial statements, financial reporting models, crypto asset white papers, and internal controls over financial reporting. All outputs are compliant with structured data standards including XBRL and iXBRL.

Regulatory Compliance

MiCAR White Papers: Comprehensive tools for Markets in Crypto Assets Regulation (MiCAR) compliance, enabling creation, validation, and publication of MiCAR-compliant crypto-asset white papers in machine-readable Inline XBRL format. Supports E-Money Tokens (EMT), Asset-Referenced Tokens (ART), and Other Crypto-Assets (OCA) following ESMA taxonomy requirements.

No Technical Expertise Required: All complexity inherent in structured financial data disclosure is handled under the hood, similar to how website content management systems use HTML and CSS constraints. Users do not need to be structured data experts or developers to create high-quality machine-readable disclosures.

Analytics and Collaboration

Visual Analytics: Users can immediately understand integrity and reliability levels of disclosures through intuitive visual dashboards displaying completeness, consistency, and compliance status. The analytics engine provides real-time feedback on data quality, highlighting potential errors, inconsistencies, or gaps.

Collaboration Infrastructure: Real-time sharing and commenting functionality enables seamless teamwork across stakeholders including team members, external auditors, and regulators. Complete version control maintains history of all disclosure documents and models.

Benefits

Repeatable production, substantial reduction of preparation time, streamlined automation, effective internal controls over financial disclosure, and reduced external audit costs. CFAs can extract and aggregate accurate and consistent financial statement data with rich meaning and context.