SMS Storetraffic
Smart, efficient, and anonymous People Counters & Analytics for the real world.
Our solution allows for simple deployment, capture, and analysis of the number of people that enter a physical location. Optionally we also capture and report occupancy in real-time.
We help Retailers, Libraries, Casinos, Universities, Places of worship, Office buildings, and other industries to analyze and take action on their people traffic trend.
For Retailers, we offer a specialized package to measure Performance on Traffic, including Conversion Rate and Service Levels. Combining POS data and staff data is easy with our direct integrations. Our Retail Equation simulator allows users to run simulations to plan sales improvement. It is also extremely beneficial as a learning tool to understand the relationship between traffic, staffing, conversion rate, and good quality service.
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RunPod
RunPod offers a cloud-based platform designed for running AI workloads, focusing on providing scalable, on-demand GPU resources to accelerate machine learning (ML) model training and inference. With its diverse selection of powerful GPUs like the NVIDIA A100, RTX 3090, and H100, RunPod supports a wide range of AI applications, from deep learning to data processing. The platform is designed to minimize startup time, providing near-instant access to GPU pods, and ensures scalability with autoscaling capabilities for real-time AI model deployment. RunPod also offers serverless functionality, job queuing, and real-time analytics, making it an ideal solution for businesses needing flexible, cost-effective GPU resources without the hassle of managing infrastructure.
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NVIDIA PhysicsNeMo
NVIDIA PhysicsNeMo is an open source Python deep-learning framework for building, training, fine-tuning, and inferring physics-AI models that combine physics knowledge with data to accelerate simulations, create high-fidelity surrogate models, and enable near-real-time predictions across domains such as computational fluid dynamics, structural mechanics, electromagnetics, weather and climate, and digital twin applications. It provides scalable, GPU-accelerated tools and Python APIs built on PyTorch and released under the Apache 2.0 license, offering curated model architectures including physics-informed neural networks, neural operators, graph neural networks, and generative AI–based approaches so developers can harness physics-driven causality alongside observed data for engineering-grade modeling. PhysicsNeMo includes end-to-end training pipelines from geometry ingestion to differential equations, reference application recipes to jump-start workflows.
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