1. Eric, for those unfamiliar with SmartOne.ai, how would you describe what you do?
SmartOne.ai helps organizations transform raw information into world-ready intelligence. We’re a global company specializing in what we call World Model Data Services, the data that powers Embodied AI and Physical AI, meaning machines that perceive, reason, and act in the real world.
We started in Madagascar in 2012 and now operate globally from Montreal. Our mission is simple: to build the human layer of AI, the contextual and causal understanding that allows intelligent systems to interact with the physical world safely and responsibly.
2. “World Model Data” sounds abstract. What does it actually mean, and how does it connect to this idea of Physical AI?
We’re entering the era of Physical AI, systems that don’t just process information, but perceive and act in the world around them. Think of autonomous vehicles, warehouse robots, drones, or surgical systems. They must understand how objects move, how cause and effect unfold, how to anticipate what happens next.
That understanding comes from World Model Data, the temporally sequenced, causally annotated information that teaches AI the physics and dynamics of reality. It’s what allows a robot to grasp an object correctly or an autonomous car to predict the motion of pedestrians.
At SmartOne.ai, we specialize in building these world models. Instead of labeling single images, we annotate sequences, movements, interactions, and outcomes. That shift from seeing to reasoning is what turns artificial intelligence into embodied intelligence.
3. How does SmartOne.ai stand out in such a competitive AI-data market?
Most providers still label static frames. We’ve moved beyond that, into temporal, spatial, and causal data modeling.
Our clients want AI that can interact with the world, in factories, hospitals, or on city streets. We deliver that through multi-frame annotation, trajectory prediction, 3D spatial reasoning, and synthetic-data generation.
We combine that depth with industrial scale, we can expand teams by 50 percent in two weeks, while maintaining over 98 percent accuracy. That mix of precision, agility, and human judgment is our competitive edge.
4. You’ve kept your roots in Madagascar and Mauritius. Why build your operation there?
Because the future of AI shouldn’t belong to just a few countries. From Madagascar and Mauritius we tap into a fast-learning, multilingual workforce naturally aligned with European and Asian time zones. It’s agile, capable, and deeply motivated.
We’ve created more than 1,000 full-time jobs and built digital-skills academies that train people in temporal annotation, 3D reasoning, and world modeling, skills that barely existed five years ago. It proves that frontier AI work can thrive in emerging markets, combining quality, scalability, and social impact.
5. How do you ensure quality and security for such complex projects?
For Physical AI, bad data isn’t just inconvenient, it can cause real-world failures. We treat data quality as a matter of safety.
Every project undergoes multi-layered QA that blends automation with human review. We operate under SOC 2 Type II, ISO, and GDPR frameworks. Our NPS is 63 and accuracy exceeds 98 percent. Each dataset is traceable, auditable, and production-ready for use in safety-critical environments.
6. There’s growing fear that AI will replace jobs. Your model seems to tell a different story.
Exactly. AI doesn’t eliminate human work, it transforms it. We’re creating a new profession: people who teach machines how the physical world works.
World Model Data requires reasoning about causality, context, and physics, things automation can’t yet replicate. By cultivating those capabilities, we’re turning the rise of AI into an opportunity for inclusion and upward mobility rather than displacement.
7. What role can Madagascar and the Indian Ocean region play in the global AI value chain?
This region is one of the hidden engines of the global AI economy. It offers multilingual talent, cost efficiency, and cultural alignment with Europe and Africa.
As AI moves toward embodied and physical systems that need real-world understanding, our ability to deliver human judgment at scale becomes even more strategic. With the right partners and investment, Madagascar and Mauritius can become global hubs for the data infrastructure powering next-generation AI.
8. Which industries are driving demand right now?
We’re seeing strong demand from sectors deploying Embodied and Physical AI, logistics and robotics, autonomous vehicles, manufacturing, and healthcare.
We’re also investing in evaluation data, the continuous-testing datasets that keep autonomous systems safe after deployment, and in synthetic data, which lets clients simulate rare scenarios and bridge the gap between simulation and reality.
9. You’ve mentioned partnerships several times. What kinds of collaborations are you seeking?
We’re building alliances with technology providers, investors, and governments that want to accelerate their AI ecosystems responsibly.
Regions like the Middle East are investing heavily in smart-city and sovereign-AI projects, which all depend on massive volumes of high-quality, context-rich data, our specialty. We combine industrial scale, quality, and ethical standards with a deep commitment to human inclusion and local empowerment.
10. Looking ahead, what’s your long-term vision for SmartOne.ai?
Our vision is to be the global reference for World Model and Physical AI Data Services, the partner enterprises and governments turn to when they want their AI to truly understand the world.
The next era of AI is not digital but physical: systems that sense, move, and decide in real environments. SmartOne.ai is building the invisible infrastructure for that future, responsibly, inclusively, and at scale.
For partnerships, media, or investment inquiries:
📩 sales@smartone.ai | 🌐 www.smartone.ai
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