WildFaces’ unique “Intuitive AI” approach requires NO Deep Learning, instead emulating complex human intelligence to address real-life complexity such as in equipment maintenance arena. As equipment failures happen at rare instances it is not possible to capture any defective data. As such, the deep learning approach is NOT practical as the starting point is usually the collection of massive datasets about the defective parts of each particular component. WildFaces’ No Deep Learning “intuitive AI” can ensure successful AI adoption in the Maintenance space as it requires less than 30 images of components in good condition for training each component model. There is also no requirement of expensive and power-hungry GPUs making AI deployment portable and cost-effective.
Our No-Deep-Learning Anomaly detection methodology detects wear & tear from normal operating performance using video, sound and smell analytics. The system looks for dents, rust, scratches, loosening parts, thickening/thinning parts, changing color, oil leakage/ water leakage, vapor/smoke etc. Our Sound AI can detect 100+ of non-conversational maintenance related sounds such as vibration, loud engine, backfire, glass breaking sound and etc. For smell AI, we can pick up any complex chemicals that could be of a concern such as toxic chemicals.
WildFaces’ patented “On-The-Move” WildAI capabilities works on IoT devices that can be mounted alongside Pan-Tilt-Zoom cameras, drones, moving robots and body-worn cameras.
See our video demo here: https://youtu.be/eE6KMoTjMrg
Visit WildFaces.ai at booth: 3E-B05, InnoEx 2025.