Physical AI Services

eInfochips Recognized as a Pioneer in
Gartner® Emerging Market Quadrant
for Physical AI Services

As organizations adopt Physical AI, they face significant challenges in bringing intelligence into real-world environments. Unlike traditional AI applications running in the cloud, Physical AI systems must process data from multiple sensors, perform real-time reasoning, operate within constrained edge hardware, and execute autonomous actions with high reliability and low latency.

eInfochips provides end-to-end Physical AI engineering capabilities across silicon, embedded systems, AI, and cloud platforms. From custom hardware design, firmware, and sensor pipelines to AI model optimization, agentic AI systems, edge deployment, and enterprise-scale orchestration, eInfochips enable companies to accelerate Physical AI adoption. With over three decades of embedded engineering expertise and strong partnerships across the semiconductor and cloud ecosystem, eInfochips helps customers transform Physical AI concepts into secure, scalable, and production-ready solutions.

eInfochips’ Physical AI Services

SENSE: The Perception Layer

  • End-to-end camera and vision pipeline development
  • LiDAR/Radar Integration
  • Sensor fusion
  • Ambient AI

DECIDE: The Intelligence Layer

  • Data Ingestion & Pre-processing
  • Algorithm / Model Inference and Optimization
  • Model Deployment & OTA/FOTA
  • Model Drift Monitoring & ML Ops
  • Agentic AI for Edge

ACT: The Autonomous Execution Layer

  • End-to-end Robotics System Development
  • SLAM, VSLAM and Navigation
  • Robotic OS and Middleware Integration
  • Simulation and Digital Twins
  • Fleet Management

SCALE: The Enterprise Integration Layer

  • Seamless integration of edge intelligence with enterprise operations
  • EIC PROPELTM for Remote device management
  • NomAIzo™ for AIOps, LLMOps, digital twins, and cloud AI orchestration

Physical AI Engineering Framework

Why eInfochips for Physical AI Services?

Silicon-Aware AI Optimization

Every model and pipeline benchmarked against thermal, FPS, and memory constraints on actual SoCs

Board-to-Blueprint Ownership

From driver tuning and HMI design to full AI lifecycle management and federated OTA updates

IP-Driven Platform Modularity

Plug-and-play IPs that scale across NVIDIA Jetson, Qualcomm DragonWing, STM32, NXP, and Ambarella platforms

3

GenAI Edge Experience

Embedded prompt execution engine (OpenAI + TFLite + FSM) running real-time STT/NLP at the edge

EdgeOps Built-In

Integrated telemetry, OTA model drift updates, rollback, and model health insights

Strategic Ecosystem Powerhouse

NVIDIA Gold Partner, Qualcomm Elite Partner, Authorized InOrbit Partner, Authorized ST Partner

Proven Experience Across Multiple AI Domains

Agentic AI, Computer Vision, NLP, Machine Learning, Enterprise AI, Edge AI, Robotics, Generative AI, AI in Cybersecurity

Success Stories

AI-Powered Autonomous Mobile Robot for Industrial Applications

Client: USA-based Global Semiconductor Manufacturer

Highlight: End-to-end design and development of an Autonomous Mobile Robot

Solution Overview:

  • Designed custom Interface Board: schematic, BOM, PCB layout, fabrication, assembly, and board-level validation​
  • Implemented CAN and USB 2.0 based communication ​
  • Ported mapping & autonomous navigation (Cartographer + NAV2) with RViz ROS2 visualization
  • ~40–50% reduction in future AMRdevelopment cycles through areusable reference platform withopen-source ROS2 stack

Edge ML for Smart Appliances

Client: US based leading Home Appliance Company

Highlight: Developed an AI-powered smart appliance platform enabling food recognition, voice-controlled interaction, and intelligent HMI for connected kitchen appliances

Solution Overview:

  • AI-based food detection and recognition using Faster R-CNN and AlexNet models
  • Voice assistant and NLP integration for hands-free appliance control
  • Edge AI deployment on Qualcomm Snapdragon 660 using SNPE
  • Smart HMI platform with image recognition, Wi-Fi/BLE connectivity, and multi-product support

Sensor Fusion for Automotive ADAS

Client: USA-based Automotive Technology Company

Highlight: Algorithm porting for side-view mirror monitoring combining multi-sensor inputs with real-time processing on automotive-grade hardware

Solution Overview:

  • Algorithm porting and optimization for different ADAS algorithms​
    • Camera Model (Fish Eye to Pinhole) ~5K LOC​
    • Object Detection ~110K LOC​
    • Pedestrian Detection, Kernelized Correlation Filters (KCF) Tracker ~80K LOC​
  • Provided PC based code developed in C++ with floating point arithmetic & trigonometric operations, sample input streams, expected output values
  • Achieved throughput of ~30fps at 720 X 480 resolution

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