Inspiration
There are many AI workflow platforms out there, but we all dreamed of having our own—one that we could fully control and customize to automate the workflows that matter most to us. The hackathon presented the perfect opportunity to bring that vision to life.
What it does
We built a comprehensive AI Workflow Manager consisting of three core components: a workflow management platform, a workflow designer, and a backend engine capable of running workflows reliably and efficiently.
How we built it
Our approach was to split the project into three distinct parts, assigning each team member responsibility for one component. The two frontend projects—the workflow manager and the workflow designer—were both developed using Bolt, allowing us to build fast, interactive interfaces.
Challenges we ran into
One of the biggest technical hurdles was figuring out how to transfer control between the two Bolt projects while maintaining state. We solved this by generating a unique token passed via the query string. This token allows the receiving platform to rehydrate the application state and display the correct data for the right user seamlessly.
Accomplishments we're proud of
Despite never meeting or speaking directly, our team collaborated smoothly across two different countries. The ability to coordinate effectively and deliver a functional, integrated product in such a distributed environment is something we’re very proud of.
What we learned
As with any complex project, clearly articulating the vision and defining detailed requirements was challenging. While we occasionally struggled, the process helped us align on the core goals and ensured that most of the time we stayed on track.
What's next for AI Workflow Manager
Looking ahead, we have an opportunity to polish the UI and turn this into a polished, user-friendly tool. A key feature we have yet to fully implement is support for parallel branches in workflow execution. If we can get this right, we believe we might have a truly valuable product on our hands.



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