Aerogility’s unique AI technology
It can seem like today, every technology solution is shouting about how it is AI-enabled. But there are many types of AI.
Some require huge amounts of data for machine learning. Some focus on autonomous decision-making. Aerogility uses model-based AI to offer a more open and transparent decision support system.
Our model-based, multi-agent AI
Aerogility is built around a proven model-based multi-agent AI framework. Essentially, we create multiple ‘agents’ that represent all the elements of your operations (assets, processes and people) — creating a digital twin of your whole business.
Each agent has parameters that determine how it interacts with the other agents in the model, and any dependencies that may constrain what can happen at any one time. For example, a repair facility having no spare capacity.
This means that when you make a change affecting one agent, Aerogility socializes the impact of this change to all other connected agents. This means you cannot adopt a strategy that is impractical or unlikely.
Delivering safe and trusted results
Unlike some other AI systems that work in the background to produce ‘the answer’, Aerogility is transparent about how it achieves its outcomes. Put simply, this means you can interrogate the process and trust the result.
This is fundamental for a ‘safe and trusted’ AI system. It is important because the kinds of decisions Aerogility supports are high-stakes and errors are costly. Aerogility will help you make the best decisions based on your objectives and experience, it won’t make decisions for you.
Learn more with AI Essentials
We’ve created a video series where experts in artificial intelligence unpack the differences between different approaches to AI and what they mean for complex operations such as aviation planning.
Professor Nick Jennings: An introduction to AI in real-world use today
Professor Nick Jennings: Why multi-agent AI systems are ideal for complex environments such as aviation
Professor Nick Jennings: How multi-agent AI delivers more accurate real-world simulations
Professor Mike Luck: How model-based AI differs from machine learning
Professor Mike Luck: The importance of safe and trusted AI
Professor Mike Luck: Using AI to model new and unforeseen events
Dr Simon Miles: Applying model-based AI to the world of aviation
Dr Simon Miles: Using AI in the real world of fleet planning
Dr Simon Miles: How to make the move to implementing AI in aviation planning