COGITO develops predictive maintenance solutions for industrial applications, combining advanced sensors, artificial intelligence, and machine learning to detect equipment failures before they occur. The company specializes in Edge AI and embedded systems, enabling real-time diagnostics directly on-site without relying on cloud connectivity. Its Cassandra device provides detailed analytics and timely alerts based on AI-driven monitoring of vibrations, thermal signatures, and acoustic signals.
Funding
Funding not disclosed
Founders
Product
Problem
Industrial equipment failures cause costly downtime and unplanned production stoppages, while traditional maintenance approaches are reactive or scheduled rather than predictive. Real-time monitoring of system health is often limited by connectivity constraints, network coverage issues, and latency that prevent immediate diagnostics in critical industrial environments.
Solution
COGITO provides complete predictive maintenance solutions that combine advanced sensors, artificial intelligence algorithms, and machine learning to detect potential faults before they occur. The company leverages infrared thermography, vibration analysis, and acoustic monitoring to identify emerging issues, with data processed in real time on cloud platforms for accurate predictions and timely interventions. COGITO specializes in Edge AI and embedded systems, enabling on-site data processing that eliminates dependence on cloud connectivity and delivers immediate, reliable diagnostics with minimal latency. The company designs, produces, and deploys custom hardware and firmware solutions for Industry 4.0 applications, including its Cassandra device for AI-powered predictive maintenance analytics and alerts.
Target Audience
Primary customers are industrial companies and manufacturing facilities seeking predictive maintenance solutions, as well as organizations with complex systems such as railway operators requiring IoT and machine learning-based condition monitoring.
Features
- Cassandra device providing AI-based predictive maintenance analytics with detailed reporting and timely fault alerts
- Edge AI architecture enabling local data processing for real-time diagnostics without cloud dependency, reducing latency and bandwidth usage
- Multi-sensor approach combining infrared thermography, vibration analysis, and acoustic monitoring for comprehensive equipment health assessment
- Custom hardware and firmware development for industrial monitoring systems, including electrical panel boards and cloud-based graphical interfaces
- IIoT and edge computing solutions for real-time diagnostics in industrial environments
- Machine learning algorithms for fault identification and machine downtime prediction