Kinzoku Blueprint Forge: AI-Powered Prototyping for Engineers

*Kinzoku Blueprint Forge: AI-Powered Prototyping for Engineers
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This post is my submission for DEV Education Track: Build Apps with Google AI Studio.
What I Built

I set out to build Kinzoku Blueprint Forge, a conceptual prototyping tool designed for engineers and industrial designers. I didn't want to create just another generic image generator; I wanted a tool that bridges the gap between a rough idea and a technical specification.

The app takes a user's text description of a mechanical concept (e.g., "A hydraulic spider leg for a mars rover") and uses a multi-model approach to generate a prototype:

Gemini 3 pro preview and Gemini 1.5 flash Flash acts as the engineer, analyzing the request to generate a Tech_Specs_V1.0 card containing hypothetical material compositions (e.g., Titanium Ti-6Al-4V), weight estimates, and dimensional envelopes.

Imagen 3 acts as the drafter, generating a high-fidelity, isometric technical blueprint on a grid background.

I also prototyped a Reverse Engineering Module (utilizing Gemini 1.5 Pro's vision capabilities) to allow users to upload existing sketches for analysis, pushing the tool from a simple generator to a comprehensive workflow assistant.

Key Prompt Strategy: To achieve the distinct "CAD software" look, I used a strict system instruction for the Imagen model:

"Always rewrite the user prompt to append: 'in the style of a high-contrast technical blueprint, white lines on dark blue grid paper, isometric view, hyper-detailed, 8k resolution.'"

Demo

Here is the live application: Kinzoku Blueprint Forge

The Blueprint Forge Interface: Generating a hydraulic system with accompanying material analysis.

The Reverse Engineering Module: A dedicated mode for analyzing uploaded schematics.
My Experience

My background is in software development and 3D modeling (Solid Edge), so my goal was to create something that felt "polished and different"—a tool I would actually want to use.

Key Takeaways:

Prompting for Precision: The biggest challenge was moving the AI away from "artistic" interpretations and toward "technical" accuracy. I learned that treating Gemini as a logic engine to structure the data before visualizing it creates a much more cohesive result.

Speed to Polish: I struggle with perfectionism, which often delays my projects. Google AI Studio allowed me to handle the heavy lifting of the backend logic instantly, giving me the time to focus on what I care about most: the UI/UX and the "feel" of the application.

The Power of Context: By wrapping the AI models in a professional, industrial-themed UI (Tailwind CSS), the perceived value of the output increased designedly. It proved that presentation is just as important as the underlying model.

I built this to push my understanding of how AI can aid in the mechanical design process, and I’m proud of the result.