AIM has developed Predeep®, a predictive maintenance management software that utilizes artificial intelligence algorithms to forecast machinery failures before they occur. This technology significantly reduces unplanned downtime by optimizing maintenance scheduling and improving operational efficiency, resulting in a 45% decrease in downtime and a 30% reduction in maintenance costs for users.
Funding
$200K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Manufacturing companies face significant challenges due to unplanned machinery downtime, leading to production losses and increased maintenance costs. Traditional maintenance schedules often fail to predict failures accurately, resulting in either premature or delayed interventions. This reactive approach hinders operational efficiency and profitability.
Solution
AIM's Predeep® is a predictive maintenance management software that leverages artificial intelligence to forecast machinery failures before they occur. By analyzing data from existing sensors and systems, Predeep® identifies potential issues and optimizes maintenance scheduling. The software integrates with various Industry 4.0 machinery, IoT sensors, and database systems to provide a comprehensive view of the entire plant. This enables proactive maintenance, reduces downtime, and improves overall operational efficiency.
Target Audience
Predeep® targets production managers and maintenance personnel seeking to minimize downtime, optimize maintenance schedules, and improve the overall efficiency of their manufacturing operations.
Features
- AI-powered health indicators that predict the risk of specific component failures
- Management and planning tools for the entire maintenance process, from intervention creation to reporting
- Hierarchical organization of machinery, linked to production lines, departments, and plants
- Modular intervention management, dividing main interventions into sub-activities
- Comprehensive reporting with key indicators and costs for informed decision-making
- Historical intervention visualization for future planning
- Intelligent alerts for high-risk failure scenarios
- Integration with existing sensors and systems
- Customizable dashboards for real-time health status monitoring