In an exclusive conversation with CXO Media & APAC Media, Rajesh Subramaniam, Founder and CEO, embedUR Systems, envisions a future where AI at the edge is powerful, cost-effective and accessible at scale and how the growing integration of AI into embedded devices calls for new competencies in machine learning, data processing, and intelligent decision-making on constrained hardware.
How does embedUR contribute to the industry with its expertise in smart devices, connectivity, and management?
Our expertise covers smart devices, connectivity, and management, establishing us as a leading provider of technology solutions tailored for today’s intricate digital ecosystem. Our skilled team utilises state-of-the-art technologies to create innovative solutions, empowering businesses and enhancing user experiences in diverse sectors.
In smart devices, we specialise in developing intuitive, user-centric technologies that simplify daily tasks, balancing functionality and thoughtful design to deliver significant user benefits. Our connectivity solutions ensure secure, reliable, and efficient communication among devices and systems by leveraging sophisticated networking technologies, crucial for supporting the growing demands of IoT and inter-device communication.
Additionally, our device and system management capabilities streamline the oversight of complex digital infrastructures, transforming device management into a simplified and efficient task. Through these comprehensive services, our clients benefit from advanced expertise in technology integration and device management, enabling them to stay ahead in their industries and effectively manage their digital environments.
What is embedUR’s strategic roadmap for its Rs 500 crore investment in India and plans to expand to 2,000+ employees by 2029?
In keeping with our strategic goals, we have worked on acquiring competencies in emerging fields like Edge AI, edge computing and cybersecurity and to this end, have already invested a fifth of the total planned outlay in 2024. This has gone towards infrastructure additions, including in-house GPU servers, Edge AI R&D, L&D and headcount growth and market outreach. These investments will continue year-on-year through 2029 as our business expands and with it, our talent base in India.
What are the key pillars of embedUR’s go-to-market (GTM) strategy, and what initiatives have been undertaken as part of this strategy?
embedUR Systems’ go-to-market (GTM) strategy is built upon several key pillars that drive its success in the embedded systems and Edge AI sectors:
- Customer-Centric Innovation: embedUR focuses on understanding and addressing the specific needs of its clients, delivering tailored solutions that enhance product value and accelerate time-to-market
- Strategic Partnerships: Collaborating with leading semiconductor companies enables embedUR to integrate advanced hardware capabilities with its software expertise, resulting in optimised Edge AI solutions.
- Investment in Emerging Technologies: The company invests significantly in AI and Edge Computing to enhance innovation and infrastructure in these domains.
- Community Engagement and Knowledge Sharing: Through initiatives like ModelNova and partnerships with organisations such as the EDGE AI FOUNDATION, embedUR fosters collaboration and accelerates Edge AI innovation within the developer community.
Recent initiatives by embedUR systems include
- ModelNova Platform Launch: A comprehensive resource hub offering pre-trained AI models, datasets, and blueprints to streamline Edge AI development.
- Strategic Partnership with Synaptics: Collaborated to support the launch of Synaptics’ high-performance adaptive MCUs, providing SDK development and AI model optimisation to facilitate Edge AI integration.
- Collaboration with STMicroelectronics: Developed advanced Edge AI applications for STM’s Edge AI HW, including image segmentation and facial recognition solutions, demonstrating the potential of AI on compact hardware.
What are the unique differentiators that set embedUR apart from other competitors in the embedded space
embedUR systems distinguishes itself in the embedded and Edge AI landscape through a combination of deep technical expertise, strategic partnerships, and a focus on commercialisation-ready solutions. Unlike traditional embedded firms, embedUR offers true end-to-end Edge AI enablement—from low-level firmware and driver development to highly optimised neural network deployment on constrained devices like MCUs. Its close partnerships with major silicon vendors position it as a trusted software partner, often contributing to early-stage SDK and AI toolchain development. This access enables embedUR to build production-grade solutions well ahead of competitors.
The company also stands out through its ModelNova platform, a community-driven initiative that provides pre-trained AI models, datasets, and deployment toolkits to accelerate Edge AI development. With a legacy in networking and system-level engineering, embedUR seamlessly integrates AI into real-world applications without compromising on performance, connectivity, or security. embedUR has also set itself apart by playing a leadership role within the EDGE AI FOUNDATION and was just the previous month presented with the ‘Partner of the Year’ award in recognition of its efforts.
How does embedded technology play a key role in driving India’s digital growth potential?
Embedded systems are quietly powering some of the most impactful shifts across sectors. In smart infrastructure, they will enable intelligent transportation networks, adaptive city services, and real-time data analytics that make urban living more efficient and sustainable. In high-tech manufacturing, embedded technologies are accelerating the “Made in India” movement, strengthening India’s role in the global value chain through automation, precision, and innovation.
Even in rural settings, embedded systems are driving cost-effective solutions that expand digital connectivity and bring essential services to underserved communities, unlocking new possibilities for inclusive growth.
How can India be developed into an “Embedded Systems Hub,” and how can its talent and strategic location be leveraged for this transformation?
With its strong talent base and strategic market potential, the country has much to gain, but a critical skills gap must be addressed to fully capitalise on this opportunity. Upskilling efforts should incorporate hardware design, firmware development, software engineering, and system integration, all as part of the same curriculum.
As IoT and edge computing gain momentum across sectors like manufacturing, healthcare, and agriculture, the demand for talent trained in sensor technologies, edge architectures, and real-time analytics is rising sharply. At the same time, the growing integration of AI into embedded devices calls for new competencies in machine learning, data processing, and intelligent decision-making on constrained hardware.
Cybersecurity is becoming equally crucial, as connected devices introduce new risks and demand expertise in secure coding and embedded system protection. Open-source tools and standards are also playing a larger role, requiring professionals to stay engaged with global development communities.
By aligning talent development with these emerging trends, India can bridge the skill gap and position itself as a leader in affordable, scalable embedded technology solutions for both domestic and global markets.
What is embedUR’s vision for cost-effective AI solutions, and how is it collaborating with global silicon players to drive affordability and accessibility?
embedUR systems envisions a future where AI at the edge is not only powerful but also cost-effective and accessible at scale. At the heart of this vision is their commitment to democratizing AI by enabling advanced machine learning capabilities on low-cost, resource-constrained hardware, particularly microcontrollers (MCUs) and edge SoCs that are widely used in consumer, industrial, and healthcare devices. Rather than relying on expensive, power-hungry processors, embedUR focuses on ultra-efficient model optimisation techniques that allow real-time AI inference on minimal silicon footprints.
To drive this vision forward with global chipset vendors, embedUR co-develops SDKs, model deployment pipelines, and reference designs that showcase how AI can run efficiently on the silicon vendor’s latest chipsets. embedUR’s ModelNova platform further amplifies this impact by offering pre-validated models and deployment toolkits, reducing engineering effort and accelerating time-to-market for AI-enabled products.
Through this ecosystem-driven approach, embedUR significantly reduces the bill of materials (BoM) for OEMs and accelerates adoption in cost-sensitive markets.
Rajneesh De, APAC Media
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