SmartOne.ai

AI is an opportunity generator — not a human crusher. And the Global South doesn’t just work for AI. It works on AI. Proud to partner with the University of Antananarivo to support the next generation of Malagasy talent. Go Madagascar 🇲🇬! Great job Jeritiana Ravelojaona and team 🚀

A Leadership Transition at SmartOne 🔄 🌱 Growing from a talented, scrappy team in Madagascar into a truly global, disciplined, and efficient organization is never an easy journey. It requires hands-on leadership, a strong culture, and values that don’t bend under pressure. 🙏 We’re grateful to Eric Raza for the role he played in helping SmartOne.ai reach this important milestone. On behalf of the entire SmartOne family, thank you — and we wish you every success in your next chapter. 🚀 As the AI landscape continues to move at incredible speed, so must we. With that in mind, Shahysta Hassim is stepping back in as CEO, and we’re excited to share what comes next for SmartOne. Here’s to a bold and exciting 2026 ahead 🎉 🎉

𝗔𝘀 𝟮𝟬𝟮𝟱 𝗰𝗼𝗺𝗲𝘀 𝘁𝗼 𝗮 𝗰𝗹𝗼𝘀𝗲, 𝗼𝗻𝗲 𝗯𝗲𝗹𝗶𝗲𝗳 𝗳𝗲𝗲𝗹𝘀 𝘀𝘁𝗿𝗼𝗻𝗴𝗲𝗿 𝘁𝗵𝗮𝗻 𝗲𝘃𝗲𝗿: 𝗔𝗜 𝘄𝗶𝗹𝗹 𝗰𝗵𝗮𝗻𝗴𝗲 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱 — 𝗯𝘂𝘁 𝗼𝗻𝗹𝘆 𝗶𝗳 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗯𝗲𝗵𝗶𝗻𝗱 𝗶𝘁 𝘁𝗿𝘂𝗹𝘆 𝗿𝗲𝗳𝗹𝗲𝗰𝘁𝘀 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱 𝗶𝘁 𝗮𝗶𝗺𝘀 𝘁𝗼 𝘀𝗲𝗿𝘃𝗲. For over a decade, this has guided SmartOne.ai’s journey. If there is one lesson we carry forward, it’s this: 𝗠𝗼𝗱𝗲𝗹𝘀 𝗲𝘃𝗼𝗹𝘃𝗲, 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝘀𝗵𝗶𝗳𝘁 — 𝘆𝗲𝘁 𝗹𝗮𝘀𝘁𝗶𝗻𝗴 𝘃𝗮𝗹𝘂𝗲 𝗮𝗹𝘄𝗮𝘆𝘀 𝘀𝘁𝗮𝗿𝘁𝘀 𝘄𝗶𝘁𝗵 𝘁𝗿𝘂𝘀𝘁𝘄𝗼𝗿𝘁𝗵𝘆 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗱𝗲𝗱𝗶𝗰𝗮𝘁𝗲𝗱 𝗽𝗲𝗼𝗽𝗹𝗲. In 2026, our ambition is clear: • 𝗺𝗮𝗸𝗲 𝗔𝗜 𝗺𝗼𝗿𝗲 𝗴𝗿𝗼𝘂𝗻𝗱𝗲𝗱, 𝗺𝗼𝗿𝗲 𝗺𝗲𝗮𝗻𝗶𝗻𝗴𝗳𝘂𝗹, 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲 𝗶𝗺𝗽𝗮𝗰𝘁𝗳𝘂𝗹 • support those who build, innovate, and take bold steps • turn potential into progress — and progress into positive change To our teams, clients, partners, and friends: 𝗧𝗵𝗮𝗻𝗸 𝘆𝗼𝘂 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝘁𝗿𝘂𝘀𝘁. We move forward together — with humility, clarity, and ambition. 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗼 𝟮𝟬𝟮𝟲: 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲, 𝗺𝗼𝗺𝗲𝗻𝘁𝘂𝗺, 𝗮𝗻𝗱 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝗽𝗿𝗼𝗺𝗶𝘀𝗲 — 𝘁𝘂𝗿𝗻 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝘃𝗮𝗹𝘂𝗲, 𝗳𝗼𝗿 𝗔𝗜 𝘁𝗵𝗮𝘁 𝘁𝗿𝘂𝗹𝘆 𝗺𝗮𝘁𝘁𝗲𝗿𝘀. SmartOne.ai — 𝗠𝗲𝗮𝗻𝗶𝗻𝗴. 𝗩𝗮𝗹𝘂𝗲. 𝗧𝗿𝘂𝘀𝘁. —————— 𝟮𝟬𝟮𝟱 𝘁𝗼𝘂𝗰𝗵𝗲 𝗮̀ 𝘀𝗮 𝗳𝗶𝗻, 𝗲𝘁 𝘂𝗻𝗲 𝗰𝗼𝗻𝘃𝗶𝗰𝘁𝗶𝗼𝗻 𝗻𝗼𝘂𝘀 𝗮𝗻𝗶𝗺𝗲 𝗽𝗹𝘂𝘀 𝗾𝘂𝗲 𝗷𝗮𝗺𝗮𝗶𝘀: 𝗹’𝗜𝗔 𝗻𝗲 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝗮 𝗱𝘂𝗿𝗮𝗯𝗹𝗲𝗺𝗲𝗻𝘁 𝗹𝗲 𝗺𝗼𝗻𝗱𝗲 𝗾𝘂𝗲 𝘀𝗶 𝗹𝗮 𝗱𝗼𝗻𝗻𝗲́𝗲 𝗿𝗲𝗳𝗹𝗲̀𝘁𝗲 𝗹𝗮 𝗿𝗲́𝗮𝗹𝗶𝘁𝗲́ 𝗮𝘃𝗲𝗰 𝗽𝗿𝗲́𝗰𝗶𝘀𝗶𝗼𝗻, 𝘀𝗲𝗻𝘀 𝗲𝘁 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝗲́. Chez SmartOne.ai, cela fait plus de dix ans que nous avançons dans cette direction. Année après année, nous avons appris une chose essentielle : 𝗹𝗲𝘀 𝗺𝗼𝗱𝗲̀𝗹𝗲𝘀 𝗲́𝘃𝗼𝗹𝘂𝗲𝗻𝘁, 𝗹𝗲𝘀 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗰𝗵𝗮𝗻𝗴𝗲𝗻𝘁 — 𝗺𝗮𝗶𝘀 𝗹𝗮 𝘃𝗮𝗹𝗲𝘂𝗿 𝗽𝗿𝗼𝘃𝗶𝗲𝗻𝘁 𝘁𝗼𝘂𝗷𝗼𝘂𝗿𝘀 𝗱𝗲 𝗱𝗼𝗻𝗻𝗲́𝗲𝘀 𝗳𝗶𝗮𝗯𝗹𝗲𝘀 𝗲𝘁 𝗱𝗲 𝗽𝗲𝗿𝘀𝗼𝗻𝗻𝗲𝘀 𝗲𝗻𝗴𝗮𝗴𝗲́𝗲𝘀. En 2026, nous voulons continuer à : • 𝗿𝗲𝗻𝗱𝗿𝗲 𝗹’𝗜𝗔 𝗽𝗹𝘂𝘀 𝗷𝘂𝘀𝘁𝗲, 𝗽𝗹𝘂𝘀 𝘂𝘁𝗶𝗹𝗲, 𝗽𝗹𝘂𝘀 𝗮𝗻𝗰𝗿𝗲́𝗲 𝗱𝗮𝗻𝘀 𝗹𝗲 𝗺𝗼𝗻𝗱𝗲 𝗿𝗲́𝗲𝗹 • accompagner celles et ceux qui construisent, innovent et prennent des risques • transformer les idées en impact — et l’impact en progrès À toutes les équipes, clients, partenaires et amis : 𝗺𝗲𝗿𝗰𝗶 𝗽𝗼𝘂𝗿 𝘃𝗼𝘁𝗿𝗲 𝗰𝗼𝗻𝗳𝗶𝗮𝗻𝗰𝗲. Nous avançons ensemble — avec humilité, réalisme et ambition. 𝗖𝗮𝗽 𝘀𝘂𝗿 𝟮𝟬𝟮𝟲 : 𝗰𝗼𝗻𝗳𝗶𝗮𝗻𝗰𝗲, 𝗰𝗹𝗮𝗿𝘁𝗲́, 𝗲𝘁 𝘂𝗻𝗲 𝗽𝗿𝗼𝗺𝗲𝘀𝘀𝗲 𝘀𝗶𝗺𝗽𝗹𝗲 — 𝗳𝗮𝗶𝗿𝗲 𝗱𝗲𝘀 𝗱𝗼𝗻𝗻𝗲́𝗲𝘀 𝘂𝗻 𝗺𝗼𝘁𝗲𝘂𝗿 𝗱𝗲 𝘃𝗮𝗹𝗲𝘂𝗿, 𝗽𝗼𝘂𝗿 𝘂𝗻𝗲 𝗜𝗔 𝗾𝘂𝗶 𝗰𝗼𝗺𝗽𝘁𝗲. SmartOne.ai — 𝗗𝗼𝗻𝗻𝗲𝗿 𝗱𝘂 𝘀𝗲𝗻𝘀, 𝗱𝗼𝗻𝗻𝗲𝗿 𝗱𝗲 𝗹𝗮 𝘃𝗮𝗹𝗲𝘂𝗿, 𝗱𝗼𝗻𝗻𝗲𝗿 𝗰𝗼𝗻𝗳𝗶𝗮𝗻𝗰𝗲.

Our most recent 𝗫𝗟𝗧 (eXtended Leadership Team) at SmartOne.ai - where we recapped 2025. Proud of the team we've built - majority women in leadership, which still feels too rare in AI, entrepreneurship (and elsewhere in the industry). Exceptional people doing exceptional work! 🚀💗 -------- Ravaka Raharivony, Onja Nantenaina, Judy Bappoo Mongelard, FCCA MBA, Miarintsoa RAMAMBATIANA, Aina Anthony RAKOTOMALALA , Lova Tahina R. , Doris DIMBIARISOA , El-Roméo HAJANIAINA , Marina Rakotondrazafy, Yanik Groeme , Ando Ravaka , Claude Hugueny , Lanto Ny Hasina ANDRIAMANANTSOA, Victor Perron, Ny Ony R. , Fanamby ANDRIAMAMONJISOA , Bakoharimina R. , Hariliva Rason , Jeritiana Ravelojaona - Not pictured: Ornella Youansamouth , Yogesh Ganeshi, Hadrien Jacomino, Edouard Naegelen , yours truly.

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𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝘀𝗮𝘆𝘀 𝗔𝗜 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝘀 𝘄𝗼𝗿𝗸𝗲𝗿𝘀. Reality from the robotics trenches: Physical AI is only as good as its training data, which requires serious human expertise. Traditional Computer Vision learns from static frames: "𝘐𝘴 𝘵𝘩𝘦𝘳𝘦 𝘢 𝘤𝘢𝘵? 𝘠𝘦𝘴 𝘰𝘳 𝘯𝘰." 𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗔𝗜 𝗻𝗲𝗲𝗱𝘀 𝗪𝗼𝗿𝗹𝗱 𝗠𝗼𝗱𝗲𝗹 𝗱𝗮𝘁𝗮: the temporal, spatial, and causal understanding of how the real world actually works. 𝗧𝗲𝗺𝗽𝗼𝗿𝗮𝗹 𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀: Not "there's a pedestrian" but "that child is about to step into traffic from behind the parked car", trajectory prediction with occlusion handling in real-time. 𝟯𝗗 𝘀𝗽𝗮𝘁𝗶𝗮𝗹 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴: That warehouse robot dodging forklifts? Multi-sensor fusion (LiDAR + camera + IMU) with depth estimation and motion planning. 𝗖𝗮𝘂𝘀𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴: Teaching humanoid robots that gripping a box here with X force means it tips, physics reasoning that humans validate frame by frame. -------- At 𝗦𝗺𝗮𝗿𝘁𝗢𝗻𝗲.𝗮𝗶, our 1,000+ specialists annotate these safety-critical scenarios for humanoid manipulation, AV systems, and autonomous operations: 𝟵𝟴%+ 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆, 𝟱𝟬% 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗶𝗻 𝘁𝘄𝗼 𝘄𝗲𝗲𝗸𝘀. Here's the truth: the 0.1% edge cases cause 90% of your deployment failures. And 𝗵𝘂𝗺𝗮𝗻𝘀 𝗮𝗿𝗲 𝘀𝘁𝗶𝗹𝗹 𝘁𝗵𝗲 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 𝗳𝗼𝗿 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗴𝗿𝗼𝘂𝗻𝗱 𝘁𝗿𝘂𝘁𝗵 𝗿𝗶𝗴𝗵𝘁. That's not a problem. That's the opportunity.

Last week, I wrote about 𝗣𝗼𝗹𝗮𝗻𝘆𝗶'𝘀 𝗽𝗮𝗿𝗮𝗱𝗼𝘅: “We, humans, know more than we can tell or explain.” Here is another one: 𝗠𝗼𝗿𝗮𝘃𝗲𝗰’𝘀 𝗣𝗮𝗿𝗮𝗱𝗼𝘅, which also challenges our intuition about intelligence. Moravec’s paradox basically states that: 𝘞𝘩𝘢𝘵’𝘴 𝘦𝘢𝘴𝘺 𝘧𝘰𝘳 𝘩𝘶𝘮𝘢𝘯𝘴 𝘪𝘴 𝘩𝘢𝘳𝘥 𝘧𝘰𝘳 𝘮𝘢𝘤𝘩𝘪𝘯𝘦𝘴, 𝘢𝘯𝘥 𝘸𝘩𝘢𝘵’𝘴 𝘩𝘢𝘳𝘥 𝘧𝘰𝘳 𝘩𝘶𝘮𝘢𝘯𝘴 𝘪𝘴 𝘦𝘢𝘴𝘺 𝘧𝘰𝘳 𝘮𝘢𝘤𝘩𝘪𝘯𝘦𝘴. • Machines (computers) today can play chess, perform complex calculations, summarize books in seconds, and even write poetry - 𝗵𝗮𝗿𝗱 𝗮𝗻𝗱 𝗲𝗳𝗳𝗼𝗿𝘁𝗳𝘂𝗹 𝘀𝘁𝘂𝗳𝗳 𝗳𝗼𝗿 𝘂𝘀 𝗵𝘂𝗺𝗮𝗻𝘀. • In contrast, machines (robots) struggle to navigate a cluttered room, run, dance, jump, or gently grasp an egg - 𝘁𝗵𝗶𝗻𝗴𝘀 𝘁𝗵𝗮𝘁 𝘄𝗲 𝗵𝘂𝗺𝗮𝗻𝘀 𝗱𝗼 𝗲𝗳𝗳𝗼𝗿𝘁𝗹𝗲𝘀𝘀𝗹𝘆, 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴. Hans Moravec (1988) theorized that the hard things for humans (playing chess, doing math) are skills we acquired only recently in our evolution, just a few thousand years ago. The easy things for us (walking, running, touching) are skills that have been built over millions of years. 𝗧𝗵𝗲𝘆’𝗿𝗲 𝗱𝗲𝗲𝗽𝗹𝘆 𝗲𝗻𝗰𝗼𝗱𝗲𝗱 𝗶𝗻 𝗼𝘂𝗿 𝗯𝗶𝗼𝗹𝗼𝗴𝘆, and we’ve had eons to optimize them. A three-year-old human toddler can balance, climb and adapt in ways that still challenge state-of-the-art robotics today. That's fascinating when you think about it, especially when you realize that on the other hand 𝗟𝗟𝗠𝘀 𝗮𝗿𝗲 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗼𝘂𝘁𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝘂𝘀 𝗼𝗻 𝗺𝗮𝗻𝘆 𝗶𝗻𝘁𝗲𝗹𝗹𝗲𝗰𝘁𝘂𝗮𝗹 𝘁𝗮𝘀𝗸𝘀. 𝗠𝗮𝘆𝗯𝗲 𝗶𝘁'𝘀 𝗷𝘂𝘀𝘁 𝗮 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗼𝗳 𝘀𝗰𝗮𝗹𝗲 𝗮𝗻𝗱 𝗯𝗿𝘂𝘁𝗲 𝗰𝗼𝗺𝗽𝘂𝘁𝗲 𝗽𝗼𝘄𝗲𝗿. Maybe one day (soon?) machines will catch up. But for now, this gap between cognitive and physical intelligence is what makes embodied AI (physical AI) so hard - and so exciting. ---------- At SmartOne.ai, we're working directly in this space, providing the training data that helps AI systems master the physical world, The next frontier in AI. 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀, 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗦𝘆𝘀𝘁𝗲𝗺𝘀, 𝗲𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗔𝗜. 𝘛𝘩𝘦 𝘬𝘪𝘯𝘥 𝘰𝘧 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘤𝘦 𝘸𝘦 𝘵𝘢𝘬𝘦 𝘧𝘰𝘳 𝘨𝘳𝘢𝘯𝘵𝘦𝘥 𝘦𝘷𝘦𝘳𝘺 𝘥𝘢𝘺, 𝘸𝘩𝘪𝘤𝘩 𝘮𝘢𝘺 𝘣𝘦 𝘵𝘩𝘦 𝘩𝘢𝘳𝘥𝘦𝘴𝘵 𝘰𝘯𝘦 𝘵𝘰 𝘵𝘦𝘢𝘤𝘩.

I love paradoxes. My Cartesian mind gets happily tickled when I encounter a new one. Reading about AI, a few months ago, I stumbled upon 𝗣𝗼𝗹𝗮𝗻𝘆𝗶'𝘀 𝗽𝗮𝗿𝗮𝗱𝗼𝘅. We, humans, know how to ride a bike. We recognize a face in a sea of strangers. We grasp subtle nuances and innuendos in conversations. We know how to do all of this BUT we can't really deconstruct this knowledge into step-by-step instructions. 𝗪𝗲 𝗸𝗻𝗼𝘄 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘄𝗲 𝗰𝗮𝗻 𝘁𝗲𝗹𝗹. And this used to make it incredibly difficult to program computers to do the same. However, with deep learning and neural networks, over the last 10 years+, this barrier has started to crumble. And now, with LLMs and generative AI, it's almost broken wide. 𝗜𝗻 𝗳𝗮𝗰𝘁, 𝗶𝗻 𝘀𝗼𝗺𝗲 𝘄𝗮𝘆𝘀, 𝘁𝗵𝗲 𝗽𝗮𝗿𝗮𝗱𝗼𝘅 𝗵𝗮𝘀 𝗯𝗲𝗲𝗻 𝗿𝗲𝘃𝗲𝗿𝘀𝗲𝗱. Where before we would say "Humans know more than they can tell," now it's "𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀 𝗸𝗻𝗼𝘄 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘁𝗵𝗲𝘆 𝗰𝗮𝗻 𝘁𝗲𝗹𝗹 𝙪𝙨." This is "𝘁𝗵𝗲 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲" in modern AI. Systems can now perform tasks that even their human creators cannot fully deconstruct, and neither can the systems themselves ... ------- But there is one domain that is still proving stubborn: 𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗔𝗜, robotics, and autonomous systems operating in the real world. Getting a robot to reliably ride a bike or delicately hold an egg remains extraordinarily difficult. A warehouse robot will get easily confused by a slight change in the environment, whereas a human will adapt in milliseconds. 𝗧𝗵𝗲 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝘄𝗼𝗿𝗹𝗱 𝗶𝘀 𝗺𝗲𝘀𝘀𝗶𝗲𝗿, less predictable, and full of that tacit knowledge that Polanyi's paradox identified. ------- This is exactly where we're positioned at SmartOne.ai . We provide 𝗵𝘂𝗺𝗮𝗻 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲 to support the perception refinement loop that physical AI systems need to navigate real-world complexity. #VLA #EmbodiedAI #WorldModels #Robotics #Sim2Real #HumanInTheLoop #PhysicalAI