Flare offers the complete Clarifai platform, plus additional tools and features designed for edge deployments. We have built a composable solution that lets you handle inference on the edge, and then sync up with our full platform in the cloud.
Anticipate potential equipment failures before they occur. Identify equipment with the greatest risk of failing, extending the life of your assets.
Use computer vision and machine learning with cameras and Edge devices to pick up details and errors much more reliably than the human eye.
Identify when workers or vehicles are stray into hazardous, off-limit zones. Alert individuals of danger and automatically shut off machinery.
Use object detection to improve domain awareness for military perimeter surveillance, maritime compliance and border security and tracking.
Create models to quickly classify and detect damaged areas (infrastructure and environment) to improve response times needed to deploy aid to people.
Improve operations and customer experience by counting customers, generating heat maps, and calculating the length of cashier lines..
Understand emotional reactions, such as liking or disliking of various products shown on shelves of a shop, or levels of stress while using a service.
Recognize vehicles, traffic signs, pedestrian, road, and objects locally, sending only information needed to perform autonomous driving to the main controller.
Fully integrated one-stop shop for every step of the AI lifecycle to maximize value from all unstructured data. Integrate seamlessly with the full Clarifai platform, so that you can develop, deploy and optimize your solution efficiently.
Run AI inference on-location, so there are no bottlenecks, delays, or service interruptions due to internet connectivity. Build ultra-efficient applications that do not experience delays while data is sent back and forth to remote data centers. Run Edge AI even without a persistent internet connection, or in completely air-gapped deployments.
Deploy into any public, private or classified software and hardware environment: on any cloud, air-gapped bare-metal or at the edge. Take advantage of edge-optimized model architectures that offer advanced predictive capabilities without taking up a ton of on-device memory.