VicRoads
VicRoads – VicTraffic web app for mobile, tablet, and desktop
Aeromedical Rapid web app
The Challenge
In June 2022, a helicopter pilot for the New South Wales Ambulance Service approached Wave with a compelling challenge: air ambulance services lacked a reliable, efficient way to assign the right aircraft to the right mission.
Dispatch decisions were largely manual, reliant on spreadsheets and siloed documentation. This made it difficult to optimise resources in a high-stakes, high-cost environment.
With helicopters significantly more expensive to operate than fixed-wing aircraft, every inefficient deployment diverted critical funds from other life-saving services. Drawing on his front-line experience, the client saw clear opportunities to improve decision-making – not only to save money, but to enhance patient care, reduce carbon emissions, and extend service reach through smarter planning.
He had already developed a sophisticated spreadsheet-based algorithm that modelled key factors such as journey time, patient urgency, refuelling needs, aircraft type, weather, and more. But he also knew a solution couldn’t remain theoretical. To be adopted by real ambulance services, it had to operate in real time. It needed to track dozens of aircraft and hundreds of missions, while providing visibility into who was doing what, where they were, and when they’d be available. The challenge wasn’t a single calculation – it was managing a dynamic, fast-moving dispatch network.
The Solution
Working with Wave, the goal was to transform this model into robust, extensible software – a decision-support engine for air ambulance dispatch. The product needed to help services make more consistent and cost-effective tasking decisions without disrupting existing workflows.
The solution would unify disparate tools into one accurate, testable, and scalable system. It needed to integrate seamlessly into live operations, support real-time tracking, and be intuitive enough for busy dispatchers and pilots to adopt with confidence. Importantly, it also had to present a polished, trustworthy brand – essential for gaining traction in a tightly knit and risk-averse industry.
The core strategy centred on accurately translating the founder’s algorithm into a scalable, robust software engine. The product had to be technically impressive, operationally reliable, and visually professional – able to build trust from the first demo.
The business case was strong: improved patient outcomes through smarter triage, savings in costs and emissions, and better risk management by factoring in pilot fatigue and shift timing.
Wave supported the founder not only in software development, but in product positioning and strategic framing. This included helping him refine his business pitch, test the algorithm’s assumptions under real-world conditions, and structure the product in a way that could scale as more data became available.
The strategy also reflected the complexities of the industry. In air ambulance services, the users (dispatchers and pilots) are often not the same as the customers (health managers, government buyers, or funders). For the solution to succeed, it had to deliver value at every level – operational, clinical, financial and strategic.
The founder’s credibility and perseverance were instrumental throughout the project. He invested heavily in research and data modelling, completed postgraduate study in the field, and was actively involved in pitching the product to organisations and at industry events. His background gave the project authenticity, while Wave helped turn his domain expertise into a viable, investor- and stakeholder-ready product.
The design process was shaped by the operational realities of air ambulance dispatch centres. The brief was narrowly defined: create a functional, intuitive interface that would blend seamlessly into users’ daily workflow. In this high-pressure environment, the tool had to feel like a natural extension of their job, not a disruption.
Wave focused on usability and clarity, removing unnecessary friction and helping users trust the recommendations.
In an industry where enterprise software is often outdated, Aeromedical Rapid set a new standard. Many comparable tools are tolerated rather than liked, simply because staff are required to use them. Wave intentionally designed Rapid to do better – to reduce user resistance, build trust, and encourage long-term adoption.
Wave also helped develop the Rapid brand identity, extending the visual system across the software interface, pitch decks, and industry event materials. This ensured professional consistency and supported the founder in making a strong first impression with potential customers and government stakeholders.
Building the product required careful translation of the algorithm into clean, testable, and maintainable software. The engine had to make nuanced recommendations based on a complex set of variables, while remaining transparent and adaptable to new scenarios.
Wave’s development team worked closely with the founder to understand edge cases, refine logic, and improve the model’s clarity. As is often the case with early product ideas, Wave helped uncover simpler, more scalable approaches to parts of the logic. This enhanced the product’s usability and reliability without compromising its accuracy.
Given the life-critical context, the system was built with a strong emphasis on automated testing and fault tolerance. Wave developed a comprehensive set of scripted test cases, ensuring the platform could handle hundreds of real-world dispatch scenarios and still return safe, appropriate recommendations. These tests continue to run automatically, safeguarding the product as new features are added or adjustments are made.
Most of the development effort went into the engine itself, rather than a wide range of interfaces. The goal was to ensure depth, accuracy, and operational safety over aesthetic surface features.
Currently the software is being used in a research trial with a large government agency. The team is continuing to iterate on new features, such as supporting multi-patient transfers, in response to feedback and pilot program requirements.
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