TRAKIVO

Turning motorsport data into better engineering decisions.

Inspiration

TRAKIVO began with a problem I, Rishi, recognised through my passion for motorsport. Racing excites me every day, every week, and every season, and I wanted to explore a future in motorsport engineering. But getting practical engineering experience can be difficult, especially for students, university teams, and people learning in lower-level racing categories where access to professional data, tools, and guidance may be limited.

Simulators and engineering tools can help, but their outputs are not always easy for beginners to understand. Students can be left looking at complicated graphs or results without a clear explanation of what happened, why it happened, or what they should test next. That can make engineering feel inaccessible instead of exciting.

We wanted to build something that makes the learning process more approachable. TRAKIVO aims to help young engineers explore ideas, understand the reasoning behind results, and learn through experimentation rather than simply being given an answer.

What it does

TRAKIVO is a motorsport engineering learning platform designed to help users move from observations to hypotheses, experiments, and better-informed decisions. It brings data and engineering reasoning together, with explanations intended to make complex concepts easier to understand.

The project is organised around four pillars:

Engineering Experiment Lab\ Users can explore a setup or engineering question, run a modelled experiment, and inspect the resulting trade-offs. Outputs should be clearly labelled as measured, calculated, simulated, inferred, or unknown, so illustrative results are not mistaken for real-world measurements.
AI Engineering Explanation\ AI can help explain results and challenge a hypothesis in accessible language. Its explanations should be grounded in available evidence and calculations, rather than inventing telemetry or presenting unsupported conclusions as facts.
Failure Centre\ Users can review unsuccessful experiments or unexpected results, investigate possible reasons, and identify what they could change or test next. This is especially relevant to student and university teams, where learning from a failed attempt can be as valuable as a successful one.
Engineering Decision Replay\ Users can revisit an engineering event or experiment, examine the available evidence, and trace how a conclusion was reached. The aim is to make it easier to investigate what happened, identify possible errors, and understand the reasoning behind a decision.

How we built it

We divided the work across the team, focusing on backend engineering, frontend development, and the website experience.

The backend work included Python for the engineering logic and API, Node.js for gateway-related functionality, and a Supabase schema for the planned data layer. We also explored a C++ component as part of the engineering architecture. The frontend uses web technologies including HTML, CSS, and React with TypeScript.

We tested and reviewed the different parts as we developed them. The project is still a prototype, so some components and workflows need further integration, validation, and refinement before they can be considered production-ready.

Challenges we ran into

One of our biggest challenges was getting the backend to behave reliably. Testing exposed issues that required us to review the code, correct problems, and check whether the engineering logic and API behaved as expected.

The frontend presented a different challenge. Turning the product idea into a clear, usable interface was harder than expected, and coding assistance did not always produce the structure or behaviour we wanted. We had to review the results rather than assume generated code was correct.

These challenges reinforced an important lesson: a working-looking interface or a successful code build does not automatically mean that an engineering product is correct. Each part needs to be tested, and the outputs need to be presented honestly.

Accomplishments that we're proud of

We are proud to have turned an idea rooted in a real learning problem into a working project structure with a frontend, backend components, telemetry-analysis code, experiment logic, tests, and a planned data schema.

We have also started shaping the project around a clear goal: making motorsport engineering concepts more accessible without losing sight of evidence and technical reasoning. The prototype is an early step, but it gives us a foundation to test, improve, and build on.

What we learned

We learned that engineering software is challenging because the different parts must work together. Writing code is only one part of the job. We also need to test assumptions, verify calculations, check integrations, explain limitations, and improve the product when something fails.

We also learned that AI coding tools can speed up parts of development, but their output still needs human review and testing. In a motorsport engineering context, unsupported claims or misleading results can undermine trust, so clarity and validation matter.

Most importantly, we learned that building a useful product requires repeated experimentation and a willingness to learn from mistakes.

What's next for TRAKIVO

Our next step is to strengthen the prototype and make the main workflow more reliable. This includes fixing and testing backend issues, improving the frontend experience, connecting the data flow between components, and validating the experiment calculations.

We also want to make evidence labels and explanations clearer, expand the examples available to learners, and gather feedback from students and university engineering teams. Over time, we aim to develop TRAKIVO into a practical learning and experimentation platform that helps aspiring engineers understand not only what a result shows, but why it matters and what to investigate next.

TRAKIVO is an evolving prototype. Simulated or illustrative outputs should be identified clearly and should not be treated as validated vehicle-performance predictions.