MNVR provides AI‑driven Asset Performance Management that creates continuously learning digital twins of industrial equipment from real‑time sensor data. The platform predicts failures, suggests maintenance actions, and automatically optimizes operating setpoints, helping heavy‑industry operators reduce unplanned downtime and maintenance costs.
Funding
Funding not disclosed
Founders
Product
Problem
Industrial operators face frequent unexpected downtime, inefficiencies, and high maintenance costs due to reliance on legacy monitoring methods that lack real‑time predictive insight.
Solution
MNVR delivers AI‑driven Asset Performance Management that continuously ingests operational data to create and update digital twins of industrial assets. These twins learn from real‑time sensor streams, enabling the platform to forecast equipment failures and recommend optimal operating setpoints. By embedding its hardware and software directly into existing control and data‑processing systems, MNVR provides seamless integration and a unified data pipeline for rapid, data‑backed decision making. The result is reduced unplanned outages, lower maintenance expenses, and higher overall production efficiency.
Target Audience
Primary customers are operators and asset managers in heavy‑industry sectors such as manufacturing, energy generation, and mineral processing who need to improve uptime and reduce maintenance costs.
Features
- Real‑time data ingestion from plant control systems feeding continuously learning digital twin models
- Predictive failure analytics that generate early warnings and recommended maintenance actions
- Automated optimization of operating parameters to maximize throughput and energy efficiency
- Edge‑computing architecture that processes data locally for low latency and high reliability
- Single, unified data pipeline that integrates with existing SCADA, PLC, and historian platforms
- Secure, cloud‑enabled dashboard delivering actionable insights and trend visualizations to operators