Air Space Intelligence
Assembly Line
Air Space Intelligence Secures $34M Series B Financing Led by a16z
Air Space Intelligence (ASI), a modern aerospace company addressing the software crisis across commercial and government sectors, announces the completion of a $34 million Series B funding round. This significant financial injection, achieved in less than three weeks, reflects strong support from new investor Andreessen Horowitz (a16z) and existing investors, including Bloomberg Beta, Renegade Partners, and Spark Capital. The allocated funds will strategically support the company’s continuous growth, foster innovation, and solidify its presence in the government sector.
ASI pioneers the development of AI-powered air operations systems to monitor and optimize flights, offering a decision advantage from the air operation center to the dynamic environment of the flight deck. The company is committed to rendering the National Airspace System (NAS) secure, safer, and more efficient for all stakeholders. ASI’s capability to rapidly field and operationalize mission-critical functionalities sets it apart in an era where legacy systems are faltering and struggling to scale to support new levels of operational complexity.
AI: how it’s delivering sharper route planning
Creating a route requires a dispatcher to answer a host of questions such as: “What is the wind today?”, “What is the best altitude for this flight?” and “Is there any military training?” Before the Flyways software, the 100 or so dispatchers at the NOC had to find answers to these questions by visiting multiple websites. These included FAA websites designed specifically for dispatchers, but that information was available only as strings of text that were hard to read.
Having decided to focus on the aviation industry, the team started spending an obscene amount of time at the NOC in an effort to understand how dispatching works and to create a user-friendly product — one that a real dispatcher could seamlessly operate when under pressure. Alaska Airlines’ employees would joke that the team was basically camping in their operations center with sleeping bags, Buckendorf says.
Flyways improves itself further by learning from a human dispatcher’s acceptance or rejection of its recommendations. When the dispatcher dismisses a suggestion, Flyways asks why: Was it because of the weather? Was the route putting an airplane uncomfortably close to somewhere it shouldn’t be? The idea is that Flyways learns from those decisions and evolves — though certain data points need to be filtered out so that the software does not simply emulate human dispatchers’ choices, stifling innovation.