An Interview Into How Leon Rutten Is Bridging Technology and Equestrian Expertise

The motivation came from a simple but persistent problem: riders forget most of what they hear during a lesson.

As a result, the real training - the days between lessons - often lacks structure and clarity. That’s where performance plateaus, and sometimes where horse welfare is unintentionally compromised.

EQ Coach-Copilot directly aligns with our mission by extending the coach’s presence beyond the lesson, ensuring continuity, better learning, and more thoughtful training decisions.

EQ Coach-Copilot is the first platform that brings together three critical components of equestrian training:

the coach’s expertise (captured via real-time audio recording), the rider’s reflection, and objective horse motion data from the EQ Saddle-Clip. 

We’ve built AI that transforms raw coaching input into structured, searchable training summaries, effectively turning each lesson into a reusable training asset.

Combined with a dataset built from hundreds of thousands of rides, this creates a new layer of intelligence in coaching that has never existed before.

My role has been to keep the focus on what truly matters: not replacing the coach, but amplifying their impact.

We’ve stayed very close to riders, coaches, and veterinarians throughout development. That grounded approach, combining technical innovation with real-world application, has been key to building something that people actually use daily.

It solves the “space between lessons.”

Traditionally, coaching is fragmented, a rider receives instructions, but retention is low, and there’s limited visibility for the coach afterward.

We turn each lesson into a structured, persistent record that riders can revisit and apply between sessions. This improves learning efficiency, reduces repetition, and enables more intentional, welfare-focused training.

There are three key differentiators:

1. Structured lesson intelligence: automatic transcription and smart summaries.

2. Unified training ecosystem: combining human input with objective horse data.

3. Human-centered design: built to support, not replace, the coach.

It’s also incredibly simple: one device, one app, no technical barrier, which is critical for adoption in a traditional sport.

This is very much a team achievement.

Our engineers, data scientists, and equestrian experts worked closely together,  but equally important, we co-created this with our users. Coaches and riders continuously tested and shaped the product.

That collaboration ensured we built something practical, not just innovative.

One of the biggest challenges was bridging two worlds: advanced AI technology and a deeply traditional sport.

It required patience, clear communication, and a strong belief that innovation must respect the craft.

We focused on simplicity and trust, making sure the technology feels natural in the training process.

We want to fundamentally change how people learn in equestrian sport.

Our goal is that riders feel supported even when they train alone, coaches can scale their expertise, and horses benefit from more consistent, data-informed decisions.

Ultimately, it’s about better performance, and better welfare.

It validates our belief that the future of sport is not about replacing human expertise, but augmenting it.

Innovation should make knowledge more accessible, more structured, and more actionable, without losing the human element that defines performance.

Beyond technology, adoption is always the real challenge.

We addressed this by focusing on immediate value, making sure that from the very first use, a coach or rider sees the benefit.

That’s what drives long-term change.

We believe coaching will become more continuous, data-supported, and globally accessible.

The traditional model of isolated lessons will evolve into a connected training journey, where every session builds on the previous one, and insights are never lost.

AI in learning and decision-making is incredibly exciting, especially when combined with real-world data.

But what excites me most is the shift toward human-centered AI: technology that enhances expertise rather than replaces it.

That’s exactly the direction we are building toward.

Start with a real problem, one that people genuinely struggle with.

Stay close to your users, simplify relentlessly, and don’t be afraid to challenge existing workflows.

And most importantly, build something that people will actually use, not just something that looks impressive on paper.