Total::Insight™ is a multi-sensor, multi-source Decision Support System (DSS) implemented by a customer into their mission-critical maritime situational awareness system. Total::Insight™ provides the customer with Artificial Intelligence (AI) and advanced Machine Learning (ML) capabilities to predict, assess, combine, and analyze maritime information from a variety of sources, including passive/active sensors, existing trackers/correlators, and soft data (e.g., weather/operator reports, web pages). Total::Insight™ sifted through this vast amount of data to capture complex relations, learn, and recognize real-time patterns, events, and anomalies. Total::Insight™ provided the customer with accurate information for decision-making and continually optimized their situational awareness. Total::Insight™ learned the underlying trends, predicted future behavior based on past ones, and un-masked targets that would otherwise have been left undiscovered. Total::Insight™ did all of this in real-time and, through numerous deployments, proved to be one of the world’s most advanced and complete Big Data and predictive analytics solutions for situational awareness in the maritime domain.

Larus integrated the Total::Vision™ Video Analytics System with our partner’s drones to develop a new “firetech” capability providing AI-based video and imagery analytics for wildfire management. This AI-based firetech was one of the first to be validated and approved by the government of Alberta to tackle the threats of wildfires in Alberta. The drones were equipped with sensitive imaging payloads that acquired imagery and videos in real-time. The drone operators then fed that data into Total::Vision™‘s AI-based object detection and classification models to accurately and precisely detect and classify hotspots. In addition, the solution automatically generated geolocated wildfire hot spots for the fire commanders to review, alert, and dispatch first responders who conducted hot spot remediation. Locations were precisely reported by Total::Vision™ in a more timely manner than achievable through manual approaches, allowing fire crews to remediate hot spots quickly. Furthermore, Total::Vision™ delivered significantly more hot spot reports than other means of detection, used fewer flying hours, and required less fire crew time while increasing fire crew safety and minimizing forest fire threats.

Larus commercial AI/ML solution allowed a large consumer packaged goods (CPG) customer to generate yearly pricing and placement plans informed by retailer and budgeting constraints. The Larus solution created promotional plans, which directly improved revenue, cost, and profit key performance indicators (KPIs). It also benefited the end consumer by providing the right product to them in the right location and time. Larus is now commercializing a SaaS AI/ML solution centered around the strategic planning of promotional events and providing capabilities such as shipment forecasting, market share analysis, demand forecasting, price forecasting, consumer profiling, innovation forecasting, and package optimization

The Larus Total::Foresight™ AI/ML product was deployed to a defence customer as an all-in-one solution for intelligence gathering and decision-making. Total::Foresight™ provided the customer with a decision support system and a systems simulation engine to improve situational awareness in air, land, and maritime approaches, in domestic and expeditionary combined and joint operations. The Total::Foresight™ Collaborative Analytics Solution provided an automated solution to improve situational awareness including the planning, tasking, and assignment of resources. Total::Foresight™’s optimized plans were generated and simulated within the tool and displayed for seamless ingestion by overloaded decision-makers who were typically inundated with many mission-critical tasks (e.g., surveillance, tracking, detection) and had a limited number of resources (e.g., drones, satellites, aircraft) at their disposal.

The Larus Total::Perception™ Systems Simulation Engine was deployed to a defence customer who used it to shorten the Tasking, Collection, Processing, Exploitation, and Dissemination (TCPED) cycle. Total::Perception™ did this by automatically cueing and tasking sensors and assets for a more efficient and timely generation of actionable intelligence. The Total::Perception™ deployment included an Artificial Intelligence (AI) Machine Learning (ML) based simulation, modeling, and multi-objective optimization engine, which provided an optimal collection architecture that efficiently satisfied the data collection requirements for missions/tasks and evaluated the relevant trade-offs identified within various scenarios. Total::Perception™ assessed the performance and effectiveness of sensors and architectures, determined, and maximized different sensor architectures, and presented the resulting analysis to the decision-makers. The assessments included the effectiveness of sensors and architectures, the value of deploying available assets, the relative merit of different asset mixes available to meet requirements, and the effectiveness in purchasing and developing new systems to meet intelligence requirements.