Larus Technologies is moving its AI-enabled decision-support platform out of the lab and into the field, following a major federal investment aimed at accelerating Canadian defence innovation.

The Department of National Defence has awarded Larus Technologies an $8.3 million Test Drive contract through its IDEaS program, backing near-term operational trials of MAABI—a tactical planning and analysis tool designed to support commanders and analysts in complex environments. The award marks a shift from experimentation to real-world application, building on operator-led exercises conducted last autumn that demonstrated sufficient maturity to justify the next phase.

At its core, MAABI is built to compress the time between analysis and action. The system combines machine learning with advanced pattern analytics to generate ranked courses of action and automated simulation outputs. It is specifically designed to support theatre-level planning processes such as Intelligence Preparation of the Battlespace (IPB) and the Operational Planning Process (OPP), where speed and clarity are critical.

To do this, MAABI pulls from a wide range of inputs—including sensor feeds, geospatial data, mapping layers, and historical activity logs—to identify movement patterns and anticipate potential adversary behaviour. It then runs rapid wargaming scenarios to test different courses of action, producing ISR collection priorities that help refine situational awareness and guide decision-making under pressure.

The next phase will see the platform tailored to Canadian Armed Forces workflows while being exercised in upcoming domestic and allied training events. These trials are expected to expose MAABI to multinational operating procedures and diverse data environments, a key step in validating interoperability and operational relevance.

Beyond the technology itself, the award underscores a broader shift in Ottawa’s approach to defence capability development. By investing in domestic solutions like MAABI, the government is signalling its intent to reduce reliance on foreign off-the-shelf systems while strengthening pathways that connect Canadian innovators to procurement.

Challenges remain. The effectiveness of MAABI in the field will depend on factors such as the quality of sensor inputs, the sovereignty and integrity of data pipelines, and the degree of trust operators place in human-machine teaming. For Larus, the focus now turns to user adoption, system integration, and demonstrating measurable mission impact as the platform enters live testing environments.

As the Canadian Armed Forces continue to scale capabilities across domains, tools like MAABI point to a future where decision advantage is increasingly shaped by the speed, precision, and adaptability of AI-enabled systems.