Inspiration
This project was inspired by the need for a flexible and structured system for planning, executing, and improving complex processes. As an engineer involved in multiple design projects, I needed a tool that could support consistent planning while allowing continuous adjustment. The goal was to transform static plans into adaptive, data-driven models that evolve with experience and feedback.
What it does
The software enables users to create detailed process plans using customizable templates that include step descriptions, task lists, resource requirements, and constraints. Process steps are organized into interconnected networks that represent dependencies, parallel execution paths, and decision points. Users can capture knowledge, assumptions, risks, and performance data at each stage. This information is integrated into a centralized knowledge base that supports feasibility analysis and intelligent recommendations. Using AI-driven analysis, Gemini 3 API, the system generates multiple execution scenarios—such as cost-optimized, time-optimized, and risk-minimized plans—and compiles them into comprehensive decision-support reports. These reports guide users in selecting and refining the most suitable execution strategy.
How we built it
The platform was developed using Google AI Studio and Gemini 3, supported by manual code refinement. Detailed prompts guided feature development, and continuous testing ensured reliability and usability.
Challenges we ran into
Key challenges included managing API quotas and validating AI-generated code. These were addressed through structured documentation, code reviews, and systematic testing.
Accomplishments that we're proud of
I developed an AI-powered contextual comparison model to evaluate process feasibility and execution readiness, enabling data-driven planning.
What we learned
I learned that AI-assisted development can function as a contextual compiler, supporting knowledge modeling and adaptive system design when guided by human expertise.
What's next for Project process Plan
Future work will focus on improving the user interface, expanding collaboration features, and deploying the platform for real-world use.
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