Where We Are Now
The next evolution of POMAI focuses on making AI-driven optimization tools easier to use by streamlining workflows, automating post-processing, and improving integration.
After working extensively with POMAI, we can confidently say the technology works. The AI-driven optimization produces real results—lighter, stronger, and more efficient components, with clear cost reductions when transitioning from traditional manufacturing to additive methods. But here’s the thing: while the technology itself is impressive, using it still requires effort. Right now, POMAI is effective but not exactly effortless for the end user. That’s what needs to change in the next phase.
POMAI has successfully demonstrated its ability to optimize antenna brackets and other aerospace components. The results speak for themselves: mass reductions, better performance characteristics, and cost savings. But working through the process—setting constraints, refining the mesh, validating results—takes time and expertise. While the software achieves its technical objectives, it still asks too much of the user.
The challenge is that topology optimization and probabilistic modeling inherently involve complexity. Engineers using POMAI need to carefully define parameters, interpret optimization outputs, and sometimes manually refine designs before final production. This isn’t a problem for an experienced analyst, but if we want more widespread adoption, we need to make the process more intuitive.
What Needs to Improve?
Streamlined User Interface & Workflow
Right now, using POMAI feels more like an expert tool than an accessible solution. The goal moving forward should be simplifying the workflow—reducing the number of steps required and improving automation in setting up optimization parameters.
Automated Post-Processing
Currently, some manual intervention is required to clean up the optimized geometries before they are ready for manufacturing. Future iterations should integrate automated post-processing features to further reduce human effort. This is actually quite hard, since people ultimately need to do some basic decision-making, and automating this is tricky at best.
Better Integration with Existing Design Workflows
Engineers aren’t working in isolation—they already use CAD and simulation software. POMAI needs to better integrate with these tools, reducing the friction of moving between platforms and ensuring optimization results can be directly implemented.
The Road Ahead
For AI-driven design tools like POMAI to be truly useful, they need to be as easy to use as possible. The next phase of development should focus on reducing complexity. We strongly believe that having users involved in each stage of development is key to achieving this.
If you’ve worked with AI-driven optimization tools before, you probably know the feeling: incredible results, but sometimes frustrating execution. The challenge now is clear—how do we take something that already works and make it effortless? That’s what’s next for POMAI.
