Introducing Airsup: AI that helps you debug your factory.
Find bottlenecks, investigate failures and test production changes in simulation before touching the real line.
Factories are already full of software. The problem is that most of it describes fragments of reality: a drawing in one system, a supplier conversation in another, a test result in a spreadsheet, a failure in somebody’s notes, and the actual line changing underneath all of them.
Airsup is our attempt to make that state legible to both people and AI. The simple version is: AI that helps you debug your factory.
Find where work is waiting. Investigate why a failure happened. Understand what a change touches. Test a production decision in simulation before the decision becomes expensive on the real line.
Manufacturing is becoming software
The most important manufacturing decisions are still physical. A machine gets moved. A supplier changes. A fixture gets redesigned. A tolerance moves. A second shift gets added. None of those decisions are easy to undo once the line has absorbed them.
But the reasoning that leads to them can increasingly happen before the physical change. Modern AI can read far more of the engineering and production record than a person can keep in working memory at once. Simulation can make assumptions explicit. A live operational model can show how a change propagates through capacity, cost, quality and lead time.
That is the direction we think manufacturing software is moving: from systems that store what happened to systems that help teams reason about what should happen next.
What Airsup is
Airsup keeps the product and production record together: goals and constraints, architecture, parts and suppliers, manufacturing steps, tests, failures, fixes, cost, lead time and the evidence behind what was actually built.
The point is not to create another place where a team manually copies information. The point is to give AI enough structured context to do useful work without pretending it knows what it does not know.
When the record is current, a question stops being a search across five systems. It becomes something the system can reason over: which revision is live, where this part is used, what changed, which requirement it affects, where the process is losing time, and what evidence supports the answer.
- One live record across engineering, suppliers, manufacturing and test
- AI that works against the actual programme context
- Traceable evidence behind recommendations and changes
From understanding the factory to changing it
The interesting part starts when the system can do more than describe the factory.
We are building toward a workflow where a team can ask what happens if a machine moves, a process time changes, a supplier slips, output has to double, or a design revision reaches production. The system should be able to model the decision, show the assumptions and expose the constraint before somebody spends money moving steel.
This is why we have been building factory simulations alongside the product. They are not meant to be pretty digital twins for a presentation. They are a way to force the production logic to become explicit enough that both a human and an AI system can inspect it.
The simulations
We have been testing that idea across very different production systems: private-jet manufacturing, humanoid robotics, robotaxis and launch vehicles.
Each model asks a slightly different question. Where does material wait? Which station sets output? What happens when a process time changes? What is the effect of a different layout? Which assumption actually controls the result?
The important thing is not that an AI can render a factory. It is whether the model helps a real team make a better production decision.
AI with human control
A manufacturing system cannot behave like a chatbot that guesses confidently and moves on. Engineering changes, production releases, purchasing decisions and safety-critical actions have owners for a reason.
Airsup can prepare the analysis, surface the dependency, assemble the evidence and propose the next action. The appropriate person still approves the decision. Where the evidence is missing, the system should say that plainly instead of filling the gap with confidence.
That distinction matters more as the models get stronger. The goal is not to remove accountability from manufacturing. It is to give the accountable person a much better view of the system they are responsible for.
Starting with the hardest hardware
We are starting with aerospace, robotics and other complex manufacturing teams because the cost of a bad record is obvious there. Part counts are high, suppliers matter, tests are expensive and a change can cross engineering, quality and operations before anyone notices the full effect.
Those teams do not need another generic AI assistant. They need a system that understands the build well enough to help with the decisions that determine whether hardware ships.
That is what we are building Airsup to become.
Start with one production problem
If you are building aircraft, robots, vehicles or other complex hardware, start with one question you wish your factory could answer faster. A bottleneck. A recurring failure. A capacity decision. A line you want to change before you touch it.
We can build from there.
Explore Airsup
Build with Airsup
Building in aerospace, robotics or complex manufacturing and want to work with us? Start with one production problem and we’ll look at it together.
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