High quality business information.
Your general purpose financial statement and regulatory reports are market signals that intelligent agents read. What do data quality issues of such reports signal about your organization today? We enable peace of mind.
Controlled information processing for accurate results.
The objective of the system is the effective exchange of business information. An important part of the system design is to eliminate “wild behaviour” by accountants when the model of a report can be modified. The system assists the user using machine-readable report descriptions.
Specify the description of a report (what is permitted) in machine-readable, and converted to human-readable, forms. Used by standard setters, regulators and anyone else specifying a report. Supports reporting models that can be modified.
Continuously improve your digital business reports
Your general purpose financial statement and regulatory reports are signals. Increasingly, those signals are being provided in digital, machine readable form.
What precisely, is signalled in your organisation's digital, machine readable reports?
When an analyst or regulator sends an intelligent agent to analyze your digital report; what is that intelligent agent going to conclude? What do the data quality issues of such reports signal about your organization?
Our Standard Business Information Engine is specifically tuned for financial reporting. It enables business professionals to work with XBRL-based digital financial reports on their terms and create provably high quality reports.
Report Processing
For XBRL-based digital financial reports
Our Standard Business Information Engine is specifically tuned for standards-based financial reporting. It emables business professionals to work with XBRL-based digital financial reports on their terms.
Report Construction
Construct digital financial reports and leverage deductive reasoning.
Visualize your report models and reports logically. Verify that you are following the XBRL technical syntax specification. Verify that you are following US GAAP, IFRS, or other financial reporting scheme logic
Standards-based Processing
Make use of high-level report objects, business rules, and a powerful logic
The powerful logic engine enables sophisticated information extraction capabilities, standards-based rules checking, enterprise quality digital financial reporting, and digital business reporting.
For XBRL-based digital financial reports
Our Standard Business Information Engine is specifically tuned for standards-based financial reporting. It emables business professionals to work with XBRL-based digital financial reports on their terms.
Construct digital financial reports and leverage deductive reasoning.
Visualize your report models and reports logically. Verify that you are following the XBRL technical syntax specification. Verify that you are following US GAAP, IFRS, or other financial reporting scheme logic
Make use of high-level report objects, business rules, and a powerful logic
The powerful logic engine enables sophisticated information extraction capabilities, standards-based rules checking, enterprise quality digital financial reporting, and digital business reporting.
Improve your signal quality today
Our customers elevate their XBRL-based digital financial reports and report creation processes with logical twins today. Contact us now to get access to the Standard Business Information Engine early access program.
Apply to join early access programCutting-edge deep-tech base platform
The Standard Business Information Engine is a special-purpose tool on top of our successful in-house cutting-edge deep-tech tool for building tools, the DFRNT.com platform. Contact us to learn more and become an early customer.
The business information engine leverages the Seattle Method developed by Charles Hoffman, CPA, the "father" of XBRL, one of the Twinfox.ai co-founders. This industrial strength method enables business professionals to construct high quality XBRL business reports using a logical conceptual model of a business report. It is a method based on two decades of research, complete with proofs and evidence.
The base DFRNT platform is developed by Philippe Höij, Twinfox.AI co-founder, a solutions architect and cybersecurity professional with more than two decades of experience from global enterprises in electronics, retail, and food packaging. His DFRNT provides a proven foundation through portable logical digital twins with a deductive reasoning datalog engine, semantic knowledge graph technologies, and a portable git-for-data approach.