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Navigation Menu
 * Home
 * Products
   * EdgeSage
   * SmartEdge
 * Verticals
   * Internet of Things
   * Edge AI in Healthcare
   * Edge AI in Retail
 * News
 * About Us
 * Contact Us




DELIVERING AI ON EDGE

Pioneering running production grade AI on the Edge

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TRAIN & OPTIMIZE

Model learns from data and establishes its ability to perform a specific
task.Optimize it for deployment on edge devices.




CONVERSION & INTERFERENCE

After optimization, the model may need to be converted into a format compatible
with the target edge device.




PRODUCTION DEPLOY

Finally, deploy the edge device with the AI model and establish a mechanism for
updates and maintenance as needed.





CHOOSE COMPUTE ARCHITECTURE

 * NVIDIA: Known for its high-performance GPUs, NVIDIA has positioned itself as
   a frontrunner in the AI hardware sector. Products like the NVIDIA A100 and
   the Jetson series are popular choices for AI development and deployment.
 * Intel: As a long-established chip manufacturer, Intel offers a range of CPUs
   for AI workloads. They have also ventured into the GPU market with products
   like the Intel Xe, aiming to challenge NVIDIA’s dominance.
 * AMD: AMD, renowned for its processors, has introduced GPUs, such as the
   Radeon Instinct series, to cater to AI and machine learning tasks. Their
   commitment to providing AI solutions is helping them gain recognition in the
   AI hardware space.

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CHOOSE COMPUTING

The Edge Server market has witnessed tremendous growth in recent years and
several key players have emerged to meet the growing demand.

We have partnered with the best in the industry to offer you a capable hardware
solution.

We offer Dell Edge, Hewlett Packard Enterprise Edgeline services and Lenovo
ThinkSystem SE based solutions.



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RUN EDGE AI MODELS

We have built software capability to run several AI models on edge devices,
including but not limited to:

 1. Convolutional Neural Networks (CNNs): These are commonly used for image and
    video analysis tasks like object detection, face recognition, and gesture
    recognition.
 2. Recurrent Neural Networks (RNNs): These are suitable for sequential data
    processing, such as speech recognition and natural language processing
    tasks.
 3. Lightweight Models: To reduce the computational and memory requirements,
    there are various lightweight models like MobileNet, SqueezeNet, and Tiny
    YOLO that can be deployed on edge devices with limited resources.
 4. Anomaly Detection Models: These models are used for identifying outliers or
    unusual patterns in data, which can be critical for applications like
    predictive maintenance in industrial settings.
 5. Custom Models: You can also develop custom AI models tailored to specific
    edge computing tasks.

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LET’S WORK TOGETHER ON YOUR
NEXT AI PROJECT

The integration of large language models with edge computing represents a
fundamental shift in the way we interact with technology. From the seamless
voice assistants in our cars to the responsive content on our devices, these
applications are transforming user experiences and enhancing operational
efficiency across various domains.

This fusion of technologies is not without its challenges, but it’s a testament
to the ever-evolving landscape of computing. As hardware improves and software
optimization techniques advance, the possibilities for large language models at
the edge will continue to expand. This shift will not only redefine how we
interact with technology but also how industries operate and innovate.

In essence, it’s a revolution at the edge, and its impact is only beginning to
be fully realized. We pioneer in establishing synergy between large language
models and edge computing, and how it’s reshaping our digital landscape.

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