Dondeteveo: Shareable Race Planning for Spectators

The Challenge

Most running tools are built for athletes, not the people trying to support them. On race day, friends and family usually work from scattered messages, rough ETA guesses, and hard-to-read race websites. That turns what should feel exciting into a coordination problem.

Dondeteveo reframes the experience around the spectator. The product had to make race plans simple to share, easy to understand, and fast to use from any phone without forcing anyone to install an app or create an account.

This project also mattered as an execution proof point: it was delivered as a fully AI-agentic development project, using AI agents across product thinking, implementation, content, and iteration. For a portfolio case, that delivery model is part of the value, not just the process behind the scenes.

What We Built

We designed Dondeteveo as a lightweight bilingual web experience that helps runners share their race plan in one link. Supporters can discover the right race edition, understand when and where to wait, and follow a plan that feels practical instead of technical.

From a delivery perspective, the work was intentionally AI-agentic end to end: shaping the product direction, building the application, refining the UX copy, and shipping an open-source implementation that can be inspected publicly. That makes Dondeteveo both a useful product and a concrete example of how AI agents can accelerate real product delivery without reducing quality.

The interface keeps the flow intentionally short:

• find the exact race edition quickly
• generate a shareable plan for supporters
• open everything directly in the browser
• support both English and Spanish from the start

That focus keeps the product aligned with the moment it matters most: race morning, when clarity beats feature depth.

Why It Works

Dondeteveo succeeds by reducing the problem to a single job: helping your crew know where to be. Search, routing, and bilingual content all support that goal instead of competing with it.

It is also built as an open-source product, which makes the implementation transparent and easy to evolve. You can see the live experience at dondeteveo.com and review the code on GitHub.

Why It Matters as a Portfolio Case

For Jerna Digital, this is more than a nice product launch. It demonstrates a specific capability: using AI agents as part of a disciplined delivery workflow to ship something real, public, bilingual, and user-centered. The result is not a demo about AI. It is a product with a clear audience, a clear problem, and a delivery approach that compounds speed with transparency.

Project Highlights

• Fully AI-agentic delivery from concept to shipped product
• Spectator-first UX instead of runner-only race data
• Bilingual launch in English and Spanish
• Link-based sharing with no app download or signup required
• Fast race discovery by edition, country, and event