Infinite Uptime utilizes AI-powered predictive maintenance analytics and piezoelectric sensing technology to monitor equipment health in real-time, providing precise diagnostics and actionable insights. This approach reduces unplanned downtime and maintenance costs, enhancing overall plant reliability across various industries.
Funding
$30.1M 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.
IL+2Founders
Product
Problem
Unplanned downtime and inefficient maintenance practices lead to significant production losses and increased operational costs across various industries. Traditional maintenance approaches often rely on reactive or time-based schedules, failing to address the actual condition and potential failures of critical equipment. This results in unnecessary maintenance, missed early warning signs, and ultimately, reduced plant reliability.
Solution
Infinite Uptime provides a comprehensive reliability solution that leverages AI-powered predictive maintenance analytics and piezoelectric sensing technology to monitor equipment health in real-time. The system offers precise diagnostics, including fault type and severity, and actionable insights to optimize maintenance schedules and minimize downtime. By continuously monitoring equipment conditions, the platform enables data-driven decisions, driving continuous improvement and optimizing performance. The service model is designed to integrate into existing workflows, providing customized views for various stakeholders and seamless data transmission to other platforms through REST APIs.
Target Audience
The primary target audience includes manufacturers in industries such as steel, chemicals & fertilizers, cement, pharmaceutical, paper, FMCG, tire, automotive, aluminum, and oil & gas, seeking to improve plant reliability and reduce maintenance costs.
Features
- 24x7 piezoelectric sensing for continuous monitoring in high-temperature, high-humidity, corrosive, and submerged environments, as well as applications with low or variable speed and load
- AI-enabled diagnostics with 99.89% accuracy in fault prediction
- Real-time monitoring of equipment, providing comprehensive insights into equipment health
- PlantOS Manufacturing Intelligence platform for in-depth examination of equipment and a comprehensive view of the entire asset portfolio
- AI Copilot (Nity) for identifying critical assets and reporting performance data
- REST APIs for seamless integration of data and insights into existing digital ecosystems
- Mobile apps for IDE configuration and bearing monitoring