In a world increasingly driven by data and automation, Yeply is taking bold steps to apply artificial intelligence inside its operations — not just as a buzzword, but as a foundation for better service, efficiency, and future-customer tools. With the help of consulting and technological partner Ainia Solutions, Yeply is building out intelligent systems that deliver real value today, and lay groundwork for transformative consumer offerings tomorrow.
Who are they
Yeply is Europe’s fastest-growing maintenance ecosystem for bicycles and bike-like vehicles. Offering services to both B2B and B2C customers, Yeply combines mobile mechanics, hubs, digital tools, and a service network across Finland, Germany, The Netherlands and Belgium. Yeply’s mission emphasizes sustainability, active mobility, and making bike ownership and maintenance simple, fast, and available close to the user. (yeply.com)
Ainia (Ainia Solutions), is a Finnish technological solution & consulting organization with decades of experience in innovation, R&D, automation, and digital transformation. Ainia helps companies define and adopt AI, build scalable architectures, integrate data systems, and automate processes in a controlled, phased way. (ainia.fi)
The Challenge
Yeply’s mechanics often need to draw information from many sources — bike manufacturer specifications, past service history, known compatibility issues, informal experience, parts catalogs, among others. More experienced mechanics often serve as the “knowledge hub” for less experienced ones, which can create bottlenecks. Also, in daily operations, time is lost whenever one must search across systems or documents to identify what service a particular bike needs, or whether a part is compatible.
Yeply saw an opportunity: If they could build AI tools that consolidate knowledge, assist mechanics in real time, and progressively learn from the data generated, then internal operations could become smoother, faster, more consistent. And once they had sufficient reliability, some of those tools could be exposed to customers as self-service features: helping them identify defects, choose parts, know what services their bike needs, even diagnose compatibility issues.
A unique foundation: 10 years of service data
One of Yeply’s biggest advantages in building AI is the dataset they’ve been gathering for over 10 years. Every bike serviced, every part replaced, every defect recorded, and every action taken has been carefully logged into the Yeply platform.
This living dataset — covering thousands of bikes, parts, actions, and defects across Europe — now provides a solid base for training and powering the AI helper. Where many companies start their AI journey with limited or synthetic data, Yeply begins with a decade of real-world, structured maintenance history. This gives their models a unique head start in accuracy, reliability, and practical usefulness for mechanics.
Building the groundwork with research
Before the implementation project began, Yeply invested in a research collaboration with the University of Amsterdam. Together, they explored the fundamentals of how AI could be applied to bike maintenance, defining the architecture, data models, and methods that would become the foundation for the proprietary AI helper. This academic partnership ensured that the implementation phase was built on solid, research-backed ground.
What they did: First phase
With Ainia’s feet on the pedals, Yeply embarked on a first phase of the project focused on internal operations. Key components of this phase included:
- AI helper code base: a software framework that mechanics can query (or that operations staff can use) for guidance, suggestions, defect identification, and for other needs.
- Vectorized database: storing data in formats that support similarity search, embedding of information (for example parts, bikes, defect types), so the system can retrieve relevant information fast, even for queries not seen before.
- Use of different models: combining (for example) smaller, specialized models for defect classification, compatibility checking, parts identification; possibly large language models for explanatory / natural-language tasks; integrating retrieval-augmented models to pull in data from the vector database.
This setup is already live in Yeply’s operations in the Benelux (Netherlands / Belgium) and in Finland.
Already an AI-powered company
Yeply has been quick to embrace AI tools across its business: from scheduling and logistics to customer interactions and data insights. This new proprietary AI helper represents the next level of competitive advantage — a tailor-made system from Ainia leveraging Yeply’s unique decade-long dataset, designed specifically for the realities of bike maintenance. While off-the-shelf AI tools bring efficiency, this tailored solution allows Yeply to differentiate, strengthen its market position, and build towards customer-facing innovation.
Why Ainia?
Ainia distinguished itself by listening carefully and understanding exactly what Yeply needed: a strategic, phased way to begin leveraging AI. Early results would be essential, but so would a clear path forward. One of Ainia’s strengths is the ability to think big, start small — mapping a high-level roadmap while delivering tangible value from the very first steps. For Yeply, this meant building a scalable foundation that could grow phase by phase, ensuring both quick wins and long-term impact. The cooperation throughout the project has been seamless, and working with Ainia has been a genuine pleasure.
What benefits are emerging
Even at this early stage, several important benefits are visible:
- Faster onboarding / training of less experienced mechanics. Because the AI helper can bring up relevant past cases, known defect patterns, and specifications, among others, the dependence on senior mechanics to teach in person is reduced.
- Reduced time wasted searching for information — manuals, compatibility information, defect histories — especially in urgent or varied repair tasks.
- More consistent diagnostics and service definitions, because the AI helper ensures common cases are handled with reference to best practices.
- Building the model / data for future customer-facing tools. As data accumulates — about defects, compatibility issues, services needed — Yeply will be in a strong position to roll out AI features to customers: helping them self-diagnose, identify what their bike needs, suggest parts, and provide other meaningful information.
Quotes
“As a mechanic, having an AI assistant that I can query for past defects, manufacturer tolerances, or compatibility saved me hours already — I feel less stuck when facing unfamiliar bike models.”
Mechanic at Yeply
“We believe this is a strategic investment. By embedding AI deeply in our operations now, we not only make things more efficient today, but we build trust and robustness so that we can confidently offer smart tools to our customers in the future. Our unique ten-year dataset gives us the confidence that we’re building on solid ground.”
Antti Känsälä, Co-founder & CEO of Yeply
”We believe in building solutions in phases: start small, create impact, and expand. With Yeply, we were able to dig into their processes, identify the most critical and time-consuming steps, and automate them with AI. That’s exactly the kind of work we specialize in—helping our customers achieve a smoother, more efficient everyday. ”
Ville Lukkari, CEO of Ainia
What’s next & broader vision
This internal-first strategy is deliberate. Yeply intends to continue refining the AI helper, gathering more data (on bike types, defects, usage, repairs), improving accuracy, and widening its coverage. Once confidence is high, Yeply plans to expose select tools to customers — for example:
- An app or web interface helping a bike owner identify an issue via photos, or uptake of suggestions based on bike model.
- Pre-service diagnostics: suggestions of services needed before problems arise.
- Compatibility checking tools: helping owners choose parts or accessories.
In doing so, Yeply continues to stay at the forefront of tech development in the bike maintenance and mobility sector, pushing not just for growth but for more intelligent, service-oriented experience, higher reliability, and more sustainable operations.
Conclusion
Yeply’s collaboration with Ainia — built on a research foundation with the University of Amsterdam — is a strong example of how a modern service company can harness AI. Not in hype, but grounded in internal workflow improvements, unique historical datasets, and model-based support for staff.