AICE for Mining
AICE delivers AI-powered computer vision solutions designed specifically for Oil & Gas operations. Our platform enhances safety, operational efficiency, and real-time decision-making across drilling sites, production facilities, and processing plants.
Operational Intelligence with AI for Mining Operations
Real-time visual monitoring of people, equipment, and critical processes.
Reduce incidents. Increase productivity. Improve operational decision-making.
Intelligent Yard and Asset Monitoring
“Real-time operational visibility.”
Managing industrial yards and operational areas requires constant monitoring of people, vehicles, and critical materials. Lack of visibility can lead to safety incidents, operational losses, and logistical disruptions .
AICE uses AI-powered computer vision at the edge to automatically detect and classify pipes , equipment, vehicles, and personnel, generating real-time traceability and alerts . This improves safety, optimizes material logistics, and increases operational efficiency in the field.
Environmental Monitoring
“The right crew. Zero improvisation.”
AICE analyzes the operational environment in real time from cameras installed on mining trucks, detecting people, support vehicles and nearby equipment during transport operations.
Using edge computer vision, the system identifies and tracks critical objects in the route , generating audible alerts for the operator when personnel or vehicles are detected within the maneuvering area.
This improves the operator's situational awareness , reduces the risk of incidents, and strengthens safety on the job site , especially in dusty, low-visibility, and high-activity operational conditions.
Safety and Productivity Monitoring
“Operational safety and efficiency, in real time.”
AICE analyzes operational and maintenance activities in real time , verifying the use of personal protective equipment (PPE) and compliance with work procedures.
Using AI-powered computer vision at the edge, the system detects unsafe conditions, identifies running tasks, and generates safety and productivity indicators during operation.
This allows for the prevention of incidents, the optimization of crew performance, and the improvement of decision-making in the field , strengthening operational discipline and the continuity of operations.
Operational Supervision
“Total visibility of the operation on the ground.”
AICE analyzes the operating environment in real time from cameras installed on the machinery , detecting personnel, equipment and anomalous conditions during maneuvers.
Using AI-powered computer vision at the edge, the system identifies operational and safety situations such as the presence of personnel in maneuvering areas, fluid leaks, or deviations in operation.
This allows for improved situational awareness of the operator, reduced risks in the field, and assurance of operational continuity , generating time-stamped visual evidence for safe and traceable management.
Access control and interventions
“Safe control of each intervention.”
AICE monitors in real time the access and activities carried out in electrical rooms , detecting personnel, equipment and safety conditions during the operation of energized panels and systems.
Using AI-powered computer vision at the edge, the system verifies the use of personal protective equipment (PPE) and detects risk situations such as the absence of gloves, goggles, or lockout/tagout (LOTO) procedures.
This allows for the prevention of electrical incidents, reinforces compliance with HSE protocols, and ensures traceability of interventions , generating time-stamped visual evidence for auditing and operational control.
Proximity alert
“Real-time collision prevention.”
AICE continuously monitors the operating environment from cameras installed on trucks , detecting nearby vehicles, loading equipment, and road conditions.
Using AI-powered computer vision at the edge, the system identifies approaching trucks, tracks their position, and generates early alerts to the operator when equipment enters the maneuvering area.
Each event is recorded with visual evidence and a timestamp, allowing for subsequent analysis and continuous improvement of operational safety.