CarGoo 2

landing page

Inspiration

All four of us were once aspiring mechanics with ODB scanners, but one look at that ancient-looking piece of technology deterred all of us from our dreams that we had once held for over a decade. This issue prompted us to become Computer Science majors and create a solution that would no longer deter aspiring mechanics who are OBD-illiterate by creating a prospective firmware and web app solution to solve this nagging problem and save their jobs.

What it does

Using a prospectively new invention that will substitute the role of an ODB scanner while also sending that data to the cloud, we built a web application that will process ODB data into real-time metrics with features built-in like a dashboard that will update values, live monitoring that visualizes data about RPM, vehicle speed, etc., diagnosing vehicles based on Diagnostic Trouble Codes, suggest repair workflows based on diagnostics, AI-generated reports that allow all users to understand of Diagnostic Trouble Codes, and an admin panel that will allow system administrators to track users, vehicles, the DTC database, and other logs.

How we built it

For the front-end tech stack, we used Next.js and Shadcn. For middleware, we used GraphQL. For backend tech stack, we used rust, diesel, utoipa, and actix-web.

Challenges we ran into

One of the major challenges we faced was interpreting and standardizing raw OBD-II data, which varies across different vehicle manufacturers and models. A big learning curve was understanding how we had to use the data and train the model in a way to be useful and applicable to our final product and systems. Building a reliable pipeline to convert this data into meaningful, user-friendly metrics required significant trial and error. Integrating multiple technologies—such as Rust for performance, GraphQL for flexibility, and modern frontend tools—also presented complexity in terms of system design and communication between components.

Accomplishments that we're proud of

We are proud of building a full-stack application that bridges the gap between complex automotive diagnostics and accessible insights for users of all technical backgrounds. From real-time data visualizations and AI-generated DTC explanations to a robust admin panel, our platform achieves a comprehensive user experience. We also take pride in our seamless team collaboration, which allowed us to ship a technically challenging product within a short development window.

What we learned

We gained a deeper understanding of how to work with complex and domain-specific data—in this case, vehicle diagnostics—and how to present that information in a way that’s both useful and approachable. We refined our skills in building scalable full-stack applications, working with tools like GraphQL, Rust, and Next.js in a real-world context. Breaking down the barriers of a technical field (vehicle diagnostics and maintenance) while also learning the ins and outs of the different tools we used was a big accomplishment.

Additionally, we also learned how to design with both technical users and non-technical users in mind, ensuring clarity without sacrificing depth. Most importantly, we learned how to break down a problem that initially felt overwhelming into smaller, solvable parts through collaboration, iteration, and a lot of coffee. Bridging the gap between our technical expertise and the level of understanding our target audience has proved to be an important consideration while fine tuning our project.

What's next for Cargoo 2

Our immediate next steps involve prototyping the hardware component that will interface with vehicles and send diagnostic data to the cloud. The hardware would need to read and process the data from the car in a way that our models can run and predict the next steps and generate informative reports with. Once the hardware is functional, we plan to integrate it with our existing web application and conduct end-to-end testing. We also aim to expand our database of Diagnostic Trouble Codes and fine-tune our AI-generated explanations to be even more accurate and helpful. Beyond that, we’re looking to gather feedback from both everyday drivers and automotive professionals to guide future improvements. Ultimately, we want Cargoo 2 to become a reliable and user-friendly diagnostic tool that bridges the gap between complex automotive data and real-world understanding.