Being precise with your asset can save millions of dollars every year. AInsight can help you, based on data.
Preparing Your Training and Testing Datasets
Transforming the Data
Building the AI Network
Train an ML model on your Data. Evaluate the accuracy of the Model. Tune hyperparameters
Send Prediction Requests to your model. Online Prediction
Monitor predictions continuously
Simple and fast configuration using Wizards
Create business cases, associate areas, define outcomes, connect them with the necessary data source and get top performance predictions.
Business Case areas
Logistics and Commerce
Easy and fast integration with the organization's data
Integrate dynamic and static data sources to your business case, all in a very easy and fast way.
Data source short-circuit DB connected
Predicción (CN08) Energy consumption
Predicción (CN10) Stock break
Predicción (CN22) Ventilation Failure
Predicción (CN23) Engine Failure
Can be used to monitor equipment or systems for signs of wear and tear, and predict when they are likely to fail. This allows for maintenance to be scheduled before a failure occurs, reducing downtime and minimizing the risk of costly repairs.
Fault detection and diagnosis
AI algorithms can be trained to detect patterns in data that indicate the presence of faults or errors in a system. By identifying the root cause of a problem, engineers can develop more effective solutions to address the issue.
Risk assessment and mitigation
AI can be used to analyze large volumes of data to identify potential risks and vulnerabilities in a system. This allows for proactive measures to be taken to reduce the likelihood of failures or errors.
Automated testing and validation
AI can be used to automate the testing and validation of software and hardware systems. This reduces the risk of human error and ensures that systems are thoroughly tested before being deployed in the field.
AI can be used to optimize energy production and consumption, reduce waste, and improve grid stability. For example, AI algorithms can analyze sensor data from power plants to identify patterns and improve efficiency, and predictive models can help forecast energy demand and adjust supply accordingly.
Supply chain optimization
AI can be used to personalize shopping experiences, optimize supply chains, and improve inventory management. For example, AI algorithms can analyze customer data to recommend products and promotions, and predictive models can help retailers forecast demand and reduce waste.
Improve logistics and reduce traffic congestion
AI can be used to improve logistics and reduce traffic congestion. For example, AI algorithms can optimize delivery routes and schedules, and predictive models can help predict traffic patterns and adjust routes accordingly.