A GenAI-built, browser-based model of the AG-3X gantry turned a spec sheet into a machine customers can spin, explode and jog, on any device.
Figures indicative; validated metrics available on request.
The robotics team was taking the AG-3X three-axis gantry to customers. The engineering was solid; explaining it was the bottleneck.
Design files needed licensed software and powerful laptops. Customers and sales could not open them.
Slides showed the gantry, not how X, Y and Z move, or what the gripper does through a pick cycle.
An outsourced walkthrough took weeks, and every design change meant paying for a re-render.
Operators and service teams could only learn the machine once a physical unit existed.
Engineers supplied the facts. GenAI wrote the 3D code. The result is a single web file anyone can open.
Select a step to see what happened.
We talked to sales, design, service and training about what each wanted to see.
Deliverable: Model brief and source data pack.
The model follows how the machine is built, so it doubles as a teaching tool.
Deliverable: Storyboard and component label list.
The model was written as code with Claude, not exported from CAD.
Deliverable: AG-3X interactive model, about 80 KB, no install.
The model went straight into the team's daily work.
Deliverable: Demo script and a sharing guide for sales.
The same pattern now applies to other stations and cells.
Deliverable: Model template, prompt pack and review checklist.
This is the AG-3X model as delivered. Drag to turn, scroll or pinch to zoom, and use the controls to explode the assembly, jog each axis or run the demo cycle.
Specifications as labelled in the model.
Each kind of machine visual now takes hours, not weeks. And the model reaches far beyond the design team.
Hours to produce, by asset type
Sessions by audience, first 8 weeks
Figures indicative; validated metrics available on request.
Customers engage longer with a machine they can move. A shareable file keeps the conversation going after the meeting.
Every label came from engineering data. GenAI wrote the code; engineers kept it accurate.
The first model took the learning. The next machine starts from a working template and a tested prompt pack.