The challenge

Background and context

Radio frequency (RF) communications in military operations are inherently bandwidth-constrained and vulnerable to jamming, interference, and environmental effects, making them unreliable in contested environments. To maintain timely and effective support across all warfighting domains, data transmitted over RF bearers should be transformed into compact, structured messages, such as eXtensible Markup Language (XML)-based Message Text Formats (MTF) or other machine-readable schemas. Such messages should be optimized for transmission over tactical networks including Low Frequency (LF), Very Low Frequency (VLF), High Frequency (HF), Ultra High Frequency (UHF), and Satellite Communications (SATCOM). This approach enables resilient, standards-based communication across domains (land, air, maritime, cyber, and space) and functional services (e.g., Joint Intelligence Surveillance and Reconnaissance (JISR), medical, logistics), in line with doctrinal requirements for pan-domain operations. 

CAF tactical messaging remains largely dependent on legacy systems. These systems are inflexible, poorly suited to machine-to-machine exchange, and increasingly misaligned with NATO’s shift toward XML-based, data-centric frameworks. CAF operators currently lack integrated, machine-readable messaging systems. As a result, they must manually convert voice, chat, and text inputs into formal military products including orders, situation reports, and Requests for Information (RFIs) to comply with command structures and coalition standards. 

Modern, pan-domain operations require that doctrinal and technical Information Exchange Requirements (IERs) be met at the speed of operational relevance, with messaging that is interoperable, standardized, and capable of supporting real-time, data-driven decision-making in joint and coalition environments. As NATO, the U.S. Department of Defense, and other Five Eyes partners adopt XML-based, machine-readable messaging frameworks, the CAF is prioritizing alignment with these emerging data models to improve interoperability, operational agility, and mission effectiveness.  

The DND/CAF are seeking innovative, interoperable, and scalable solutions that will contribute to the development of a next-generation digital military RF messaging and data exchange. Innovators are invited to propose technologies, platforms, or integrated systems. 

Essential outcomes

The proposed solution must:

• Be a software-based capability (e.g., standalone application, plug-in, middleware, or service)
• Support DND/CAF operations across all domains (land, air, maritime, space, cyber) and functional services (e.g., C2, intelligence, logistics) 
• Implement structured, standards-compliant messaging (e.g., MTF-XML) and data exchange protocols (e.g., National Information Exchange Model (NIEM), NATO Core Data Framework (NCDF)), with documented interfaces for importing, exporting, and exchanging data using those standards
• Be configurable and modular, supporting Character-Oriented Information Exchange (COIE) processing and designed for future extensibility (e.g., to accommodate emerging or disruptive standards)
• Provide documented interfaces and standards-based data exchange mechanisms to support system integration, including use within experimentation, simulation, and wargaming environments

Desired outcomes

The proposed solution should include capabilities such as, but not limited to, the following:

• Operability in Denied, Degraded, Intermittent, and Low Bandwidth (DDIL) environments, with scalability to support evolving mission requirements and force structures
• Enhanced user experience features such as intuitive interfaces, multilingual support, automation of routine workflows, and built-in decision aids
• Standardized, documented integration interfaces (e.g., application programming interfaces (APIs)) to enable integration with at least one service management or service monitoring tools or platforms (e.g., ServiceNow, Splunk, Dynatrace, Microsoft System Center, Azure Monitor, etc.)
• Readiness for future integration of artificial intelligence (AI) and machine learning (ML) capabilities to support data fusion, intelligent message prioritization, and real-time decision support