Orbit

Inspiration

When I tried to manage my life using emerging AI agent systems, I realized how complex and impractical they were. Setup was overwhelming, and server costs were unjustified for individual use. Even when trying to share resources with friends, adoption was difficult—most people don’t realize how useful AI agents can be. This led me to build Orbit: a simple, accessible AI agent designed for everyday users, focused on practical and meaningful tasks.

What it does

Orbit is an AI agent that automates everyday digital tasks. It reads your emails to track expenses, categorizes them automatically, and helps you stay aware of your financial activity. It can also manage incoming emails, generate documents in Word and Excel, and execute simple code to automate repetitive business tasks—like transferring data between formats or documents.

How we built it

Orbit was built as a full-stack system using multiple languages depending on the task: Python for AI orchestration, Java for backend services, and Go for lightweight integrations. It leverages OpenRouter to access large language models, enabling intelligent decision-making. Auth0 is used to securely connect the agent with external services like Gmail, allowing it to act on behalf of the user. The agent operates autonomously for routine tasks (like expense tracking), while keeping the user in control for sensitive actions such as email management.

Challenges we ran into

We faced multiple technical challenges throughout development. Frontend issues with React caused inconsistent builds across environments, which led us to experiment with Docker—introducing its own complexity. Integrating the Gmail API and stabilizing the database also proved difficult. Deployment was another major hurdle, especially switching between containerized and non-containerized setups. Additionally, due to time constraints and unexpected setbacks like hardware loss, some planned features (such as deliveries and reservations) couldn’t be completed. The Auth0 integration, while functional, still requires further refinement and debugging.

Accomplishments that we're proud of

We are especially proud of Orbit’s financial module, which delivers a clean and intuitive experience for tracking expenses. The visual design, particularly the planetary animations, creates a unique and engaging interface that stands out from typical AI tools. We also achieved efficient context management, allowing the agent to retain useful memory without excessive resource consumption. Additionally, we developed a system that minimizes token usage for financial tasks, reflecting our philosophy of building efficient and accessible AI. What truly sets Orbit apart is its vision—not as a cold, cyberpunk system, but as a tool designed to make technology feel intuitive, human, and even beautiful.

What we learned

We learned the importance of proper tooling and architecture decisions early on. Docker proved to be essential for consistency across environments, and in hindsight, it should have been the first thing we set up. We also explored Go, which opened new perspectives on building efficient and lightweight services. Structuring the system by functionality rather than purely technical layers turned out to be a very effective approach. Overall, we realized how much unnecessary complexity can be avoided with the right tools and planning from the start.

What's next for Orbit

Our immediate focus is stabilizing the platform by fixing existing bugs and improving reliability. From there, we plan to expand Orbit’s capabilities with features like SMS integration, a more complete financial system, delivery and reservation services, and even investment tracking. We also aim to introduce background task automation so the agent can operate continuously on behalf of the user. Long-term, our goal is to build a scalable service that remains efficient and accessible, growing sustainably as more users adopt Orbit.

Bonus Blog Post

Orbit started as a simple idea: making AI actually useful for everyday life. Not as a chatbot, but as an agent that could take action. However, building that vision turned out to be far more complex than expected.

One of the biggest challenges was integrating external services reliably. Using Auth0 to connect with APIs like Gmail introduced multiple edge cases, especially around authentication and token handling. At the same time, we were working with OpenRouter to manage AI interactions, which pushed us to think carefully about efficiency. Instead of maximizing AI usage, we focused on minimizing unnecessary token consumption—this is where our approach aligns with the idea of a “Token Vault,” treating tokens as a resource to optimize rather than waste.

Another major hurdle was environment consistency. We faced issues where the frontend worked on one machine but failed on another. Docker eventually became a key part of our workflow, allowing us to standardize the environment and avoid those inconsistencies.

Despite the challenges, we achieved a system that can automate real tasks: analyzing emails, generating documents, and assisting with communication workflows. More importantly, we built it with a clear philosophy—AI should feel lightweight, efficient, and genuinely helpful, not overwhelming or expensive.

This project taught us that building AI products is not just about intelligence, but about reliability, usability, and thoughtful resource management.

Built With

• auth0
• docker
• go
• java-(spring-boot)
• openrouter
• postgresql
• python
• react