An AI use case is a set of activities taken to reach a specific goal from a business or customer perspective, which involve a substantial application of artificial intelligence to enhance efficiency.

ENAIA is adaptable to any uni or multimodal use case need

Some use cases in which ENAIA can excel are:

Detecting defects in manufacturing processes using a combination of visual (image) and sensor data.

Predictive maintenance by analyzing multimodal data from machines to anticipate equipment failures.

Disease diagnosis using a combination of medical images (such as X-rays or MRIs) and patient data.

Monitoring patients for unusual vital signs or behavior through a combination of data sources.

Fraud detection in financial transactions by analyzing text descriptions, transaction amounts, and customer data.

Anomaly detection in stock trading by considering market data, news articles, and social media sentiment.

Pest detection using a combination of image recognition and environmental sensor data.

Detecting fraudulent transactions by analyzing transaction history and user behavior.

Anomaly detection in customer reviews by considering both text and sentiment analysis.

Predictive maintenance of energy infrastructure by analyzing sensor from equipment.

Detecting unusual energy consumption patterns through a combination of data sources.

Anomaly detection in autonomous vehicles by combining sensor data (LIDAR, radar, cameras) with navigation information.

Predictive maintenance for vehicles by analyzing engine sensor data and service records.

Identifying network intrusions and cyberattacks by analyzing network traffic logs and system behavior.

Detecting anomalies in user access patterns using user login data and system logs.

Detecting pollution and environmental hazards through the analysis of sensor data, satellite imagery, and weather data.

Identifying anomalies in climate patterns by analyzing historical climate data.

Anomaly detection in supply chain disruptions by considering shipping data, inventory levels, and external factors.

Monitoring and optimizing warehouse operations by analyzing sensor data and order processing information.

Detecting network anomalies and performance issues by analyzing call data records, network logs, and customer complaints.

Identifying unusual patterns in customer call behavior and billing data.

Predictive maintenance for drilling equipment by analyzing sensor data from oil rigs.

Detecting anomalies in pipeline operations through a combination of sensor data and geographic information.