COMPREDICT utilizes AI-based virtual sensors to extract actionable vehicle insights from existing onboard sensor data, enabling enhanced vehicle health monitoring and predictive maintenance. This technology reduces the need for additional hardware sensors, lowering costs while improving maintenance efficiency and vehicle utilization.
Funding
$20.8M 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
The increasing complexity of modern vehicles and the growing demand for predictive maintenance require more sophisticated and cost-effective methods for monitoring vehicle health and usage. Traditional hardware sensors can be expensive to install and maintain, limiting the ability to gather comprehensive data for accurate predictive analytics. This creates a need for solutions that can leverage existing vehicle data to provide actionable insights without adding significant hardware costs.
Solution
COMPREDICT offers a virtual sensor platform that uses AI to extract vehicle insights from existing onboard sensor data, enabling enhanced vehicle health monitoring and predictive maintenance. By applying machine learning algorithms to readily available vehicle data, the platform creates virtual sensors that act as digital twins of hardware sensors, predicting component wear and tear, detecting anomalies, and forecasting end-of-life. This approach reduces the reliance on physical sensors, lowering costs and integration efforts while providing new measurement capabilities and improving maintenance efficiency. The virtual sensors can be deployed in the cloud or embedded within the vehicle's electronic hardware, offering flexibility and scalability for various applications.
Target Audience
The primary target audience includes vehicle OEMs, Tier 1 suppliers, and fleet solution providers seeking to reduce costs, enhance vehicle performance, and offer innovative digital services.
Features
- AI-based virtual sensors that replace or augment traditional hardware sensors
- Predictive maintenance capabilities, including component wear forecasting and anomaly detection
- Vehicle health monitoring for assessing the condition of critical components like tires, brakes, and batteries
- Virtual mass sensor for continuous payload monitoring without physical sensors
- Integration with BlackBerry IVY middleware for streamlined deployment and data access
- Cloud-based or embedded deployment options for flexibility and scalability
- Virtual sensor pipeline for agnostic virtual sensor calibration
- Support for a wide range of vehicles, including gasoline, diesel, hybrid, and electric vehicles