Tejo Chalasani
CEO
Utpatti
The startup offers a cloud-based predictive maintenance platform that utilizes machine learning algorithms to forecast equipment failures and assess the remaining useful life of machinery. By providing early warning notifications and actionable insights, the platform helps businesses minimize unplanned downtime and extend the operational lifespan of their assets.
- Artificial Intelligence
- Data & Analytics
- Enterprise Software
- Industrial Automation
- Internet of Things
- Software Only
Updated 6 months ago
Funding
Raised to date
$620KRaised 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.
Funding rounds are not available yet.
Founders
Product
Problem
Manufacturers often struggle to fully utilize the vast amounts of machine data collected by sensors, leading to missed opportunities for predictive maintenance and optimized equipment uptime. Traditional predictive maintenance solutions may require transferring sensitive operational data to external clouds, raising concerns about data sovereignty and security.
Solution
Utpatti offers a decentralized AI-powered predictive maintenance platform that enables manufacturers to leverage existing sensor and breakdown data to train models for predicting equipment failures and suggesting resolutions, all without transferring data to an external cloud. The platform utilizes a decentralized AI framework to facilitate knowledge sharing across plants, driving company-wide innovation while ensuring data security and control. By providing early failure warnings and remaining useful life predictions, Utpatti helps businesses minimize unplanned downtime, optimize maintenance schedules, and reduce warehouse costs through just-in-time parts ordering.
Target Audience
Utpatti is designed for industrial plant managers and smart maintenance leads seeking to reduce unplanned machine breakdowns, increase plant production, and maintain control over their operational data.
Features
- Decentralized AI models that enable knowledge sharing across plants without data transfer
- Predictive maintenance with over 90% accuracy using sensor data and limited breakdown history
- Early warning notifications of potential failures up to 6 months in advance
- Remaining useful life prediction for connected equipment
- Health status monitoring of equipment with categorized status indicators
- AI-powered process change suggestions to prevent future failures
- Customizable application dashboard for machine health prediction
- Seamless integration with existing operational systems and infrastructure