Our Story: A Journey Defined by Real-World Execution


We are a deep-tech engineering company shaped not by theory, but by deployment. Our journey has been defined by building systems that had to work—under load, at scale, in live environments where failure was not an option. For over two decades, we have operated where latency, throughput, and reliability are absolute constraints, proven not in labs but in production.

Each step of our journey took us deeper into the same core challenge: how to sense, move, interpret, and act on data in real time—across physical and digital worlds—at scale. What may look like multiple domains today is the result of progressively harder problems encountered in the field, each one extending our engineering depth and system-level understanding.

Media Intelligence: Learning Real-Time at Scale

Our journey into real-time systems began with live media—one of the most unforgiving production environments. At global scale, even milliseconds matter.

  • Sub-second real-time streaming (<1s) for global video platforms: low latency, high scale, zero buffer.
  • Production-grade WebRTC OTT stacks, GPU/AI-driven media pipelines, and interactive Edu-Tech platforms.
  • AI-driven video intelligence and control-room automation, enabling richer engagement and operational efficiency.
  • These deployments laid our foundation in deterministic data movement and real-time intelligence extraction.

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From 5G to 6G: Taking Control of the Network

As media platforms scaled, it became clear that real-time performance could not rely on networks as black boxes. We moved deeper—into the network itself. Our work in 5G was driven by production realities: accelerating R&D to deployment, reducing operational complexity, and guaranteeing performance under live traffic. Field deployments shaped our end-to-end IP.

  • End-to-end 5G systems, taking customers from lab to live production faster.
  • 5G Core-in-a-Box + O-RAN, optimized at 10–25 Gbps for real-world workloads.
  • Terabit-scale throughput and ultra-low latency, using VPP + DPDK data planes.
  • Seamless operation across Intel, NVIDIA, Broadcom, and ESXi VMXNet3 environments.
  • Autonomous, self-learning networks that adapt, optimize, and heal in production.
  • This phase transformed our expertise from application-level performance to full-stack, carrier-grade systems—laying the groundwork for 6G readiness.

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RFID: Bringing the Physical World into the System

As networks and platforms matured, the next challenge came from the physical world—where noise, inconsistency, and scale collide. Large-scale RFID deployments forced us to solve real-time identity, tracking, and orchestration across thousands of devices operating in uncontrolled environments.

  • Real-time supply chain visibility, reducing human error through RFID + IoT integration.
  • End-to-end RFID ecosystems, from edge devices to Kubernetes-based platforms.
  • High-performance RFID pipelines, engineered for scale, precision, and reliability.
  • BLE + RFID integration, delivering cost-effective, energy-efficient solutions proven in the field.
  • These deployments anchored our ability to bridge physical events with digital intelligence—reliably and at scale.

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Micro-Robotics & IoT: Pushing Intelligence to the Extreme Edge

Everything we learned—real-time data movement, deterministic networking, large-scale orchestration—ultimately converged at the edge. Field deployments demanded mastery over constrained compute, power, thermal limits, and real-time control, all while maintaining security and scalability.

  • Embedded systems engineered for production, balancing real-time guarantees, resource limits, and security.
  • Micro-robotics platforms, solving power, control, communication, and scalability challenges.
  • Advanced electronic board design, addressing heat dissipation, signal integrity, and miniaturization for optimal performance and cost.
  • Here, intelligence is no longer centralized—it lives at the edge, acts instantly, and must never fail.

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One Journey- Proven in Production


Our story is not about disconnected domains. It is about a single, continuous journey—each stage shaped by real-world deployments that forced us deeper into the system stack.

From live media to networks, from networks to physical sensing, and from sensing to autonomous edge intelligence, every step was earned in production. What we bring today is not experimentation, but field-hardened, production-proven deep-tech engineering.

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