Skip to main content
embedded iot edge-computing microcontrollers rtos arm esp32 edge-ai firmware industrial-iot

Embedded & Edge Computing

Custom embedded systems and edge computing solutions for resource-constrained environments.

Embedded & Edge Computing

Embedded Systems Development

We design and develop embedded systems for industrial, consumer, and research applications. Our expertise spans the full development lifecycle, from hardware selection to firmware development and deployment.

Full-Stack Embedded

From bare-metal firmware to edge AI inference, we handle the complete embedded software stack—ensuring optimal performance within your power and resource constraints.

System Architecture


flowchart TB
    subgraph Hardware["Hardware Layer"]
        A[Sensors] --> B[MCU/SoC]
        C[Actuators] <--> B
        D[Power Management] --> B
    end

    subgraph Firmware["Firmware Layer"]
        B --> E[RTOS/Bare-Metal]
        E --> F[Device Drivers]
        F --> G[Application Logic]
    end

    subgraph Edge["Edge Intelligence"]
        G --> H[ML Inference]
        H --> I[Local Decision]
        I --> J[Cloud Sync]
    end

    style Hardware fill:#e0f2fe,stroke:#0284c7
    style Firmware fill:#fef3c7,stroke:#d97706
    style Edge fill:#dcfce7,stroke:#16a34a

Core Capabilities

CapabilityTechnologiesUse Cases
MCU ProgrammingARM Cortex-M, ESP32, STM32, RISC-VIndustrial control, consumer devices
Real-Time OSFreeRTOS, Zephyr, bare-metalTime-critical applications
CommunicationUART, SPI, I2C, CAN, Modbus, MQTTSensor networks, industrial protocols
Low-Power DesignSleep modes, power gating, energy harvestingBattery-powered devices
Industrial IoTRuggedized systems, EMC complianceHarsh environments

Development Platforms

PlatformBest ForKey Features
ESP32/ESP-IDFCost-effective IoTWiFi/BLE, dual-core, low cost
STM32Industrial gradeWide range, robust ecosystem
Raspberry PiEdge computingLinux-based, rapid prototyping
JetsonEdge AIGPU acceleration, CUDA support
Custom HardwareSpecialized needsWorking with your hardware team

Edge AI

AI at the Edge

Process data locally for lower latency, reduced bandwidth, and enhanced privacy. Our edge AI solutions run efficiently on resource-constrained devices.
CapabilityImplementationBenefits
Model DeploymentTensorFlow Lite, ONNXCross-platform inference
On-Device InferenceQuantization, pruningLow latency, offline operation
Federated LearningDistributed trainingPrivacy preservation
Privacy-Preserving MLOn-device processingData sovereignty

Optimization Techniques

  • Model Quantization: INT8/INT4 for smaller footprint
  • Pruning: Remove redundant weights
  • Knowledge Distillation: Smaller student models
  • Hardware Acceleration: NPU/DSP utilization

Implementation Process

  1. Requirements Analysis: Define constraints, interfaces, and performance targets
  2. Architecture Design: Hardware selection and system design
  3. Firmware Development: RTOS integration and driver implementation
  4. Edge AI Integration: Model optimization and deployment
  5. Testing & Validation: Hardware-in-the-loop testing
  6. Production Support: Manufacturing support and field updates

Need embedded expertise? Contact us to discuss your requirements.

Back to Services