This project started as a personal experiment: designing a unique planter specifically for xerophytic plants (plants adapted to low-water environments), and doing it using information gathered and organized with the help of ChatGPT.

The goal wasn’t to “let an AI design a product for me”, but to use a conversational workflow to explore: plant requirements, substrate behavior, drainage and aeration needs, and practical considerations of geometry and materials—then translate that into an object that can be printed, tested, and improved.

Right now, the first version is complete. I used it to validate the theoretical basis and to test several functional ideas intended to optimize the growing environment (mainly around water control, aeration, and long-term plant stability).

I’m currently running growth tests with different xerophytic species to collect observations and compare how they perform under the same conditions. Once I have enough data, the next phase will be more systematic: adding digital measurement of the micro-environment to better understand what the plant is “experiencing” and refine the design based on real signals.

I strongly believe in closing the gap between theory and practice. Product development needs experiments: to validate assumptions, measure real behavior, and discover what we didn’t predict. This project is exactly that: a functional concept test that will later move into impact analysis and viability exploration if the results justify it.