Turing Edge AI

Microservices-based solution for AIoT use cases.

It runs AI models at the EDGE and applies business rules. Connects AI, IoT (sensors, drones, robots), cloud, and telecommunications.

Test for free

Use cases

Smart Manufacturing

Pixel-level segmentation to detect and classify anomalies. Digital twin to optimize processes.

Detect flaws in real time. Ensure safety measures are being followed. Optimize production lines.

Smart Facilites

Pixel-level segmentation to detect and classify anomalies. Digital twin to optimize processes.

Detect incidents in real time to optimize security in big facilities with minimal latency. Optimize crowd control.

Smart Roads

Pixel-level segmentation to detect and classify anomalies. Digital twin to optimize processes.

Detect incidents in real time to optimize road security. Digital twin simulations allow you to optimize traffic and fees.

Smart Cities

Pixel-level segmentation to detect and classify incidents. Digital twin to optimize processes.

Coordinate and optimize transport, security, and waste management systems. Optimize crowd control.

Capabilities

Detection

Object detection to check PPE or find facilities from aerial photos.

Classification

It allows making decisions on product quality or features to sort and screen in automation processes.

Crowds

People count, statistics by age or gender, for crowd control and security measures.

Anomalies

Detection of anomalies in real time and classification of events and alerts. It allows the monitoring of industrial and retail environments, even in large facilities.

Inspections

Automatic processing of thousands of sample images per hour to search, label, and classify results from inspections.

IoT Hardware

The system incorporates sensors and cameras (regular, thermical, or x-ray) to read the environment. These may be mobile thanks to drones or robots.

Implementation in
Wi-Fi or 5G

Connect the edge devices to the cloud via Wi-Fi, 5G, or the protocol network that fits better to ensure minimal latency.

Simulation in digital Twins

Feed a digital twin the data taken on the edge to simulate processes and try improvements. Implement in the physical twin the optimal decisions discovered on the digital twin.

Turing Edge AI

Microservices-based solution for AIoT use cases.

Use cases

Smart Manufacturing

Pixel-level segmentation to detect and classify anomalies. Digital twin to optimize processes.

Smart Facilites

Pixel-level segmentation to detect and classify anomalies. Digital twin to optimize processes.

Smart Roads

Pixel-level segmentation to detect and classify anomalies. Digital twin to optimize processes.

Smart Cities

Pixel-level segmentation to detect and classify incidents. Digital twin to optimize processes.

Capabilities

Detection

Classification

Crowds

Anomalies

Inspections

IoT Hardware

Implementation in Wi-Fi or 5G

Simulation in digital Twins

Implementation in
Wi-Fi or 5G