Sensemore is an end-to-end machine health platform that utilizes AI-driven analytics and data acquisition devices to predict equipment failures 2-4 months in advance. This proactive approach reduces maintenance costs by 25% and minimizes downtime, enabling more sustainable industrial operations.
Funding
$300K 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
Unplanned downtime and equipment failures in industrial operations lead to increased maintenance costs, reduced productivity, and higher carbon emissions due to inefficient energy consumption and resource waste. Traditional maintenance strategies often lack the ability to predict failures in advance, resulting in reactive repairs and suboptimal performance.
Solution
Sensemore provides an end-to-end machine health platform that leverages AI-driven analytics and data acquisition devices to predict equipment failures 2-4 months in advance. The platform combines wireless sensors, edge computing, and a cloud-based analytics platform to monitor the condition of critical assets in real-time. By analyzing vibration, temperature, current, and voltage data, Sensemore's AI algorithms identify anomalies and predict potential failures before they occur. This proactive approach enables businesses to optimize maintenance schedules, reduce unnecessary repairs, minimize downtime, and decrease carbon footprint. The platform also offers a maintenance bot that analyzes sensor data in real-time and sends notifications to maintenance teams.
Target Audience
Sensemore targets industrial companies in sectors such as cement & mineral, iron & steel, pharmaceutical, and FMCG, seeking to improve equipment reliability, reduce maintenance costs, and enhance sustainability through predictive maintenance.
Features
- Wireless vibration, temperature, current, and voltage sensors for comprehensive machine health monitoring
- Edge computing capabilities for real-time data processing and anomaly detection
- Cloud-based analytics platform with AI-powered predictive maintenance algorithms
- User-friendly web interface for visualizing machine health data, trends, and predictions
- Automated alerts and notifications for potential equipment failures
- Integration with existing maintenance management systems (CMMS)
- Maintenance bot for real-time analysis and notifications via messaging apps
- Support for Failure Mode and Effects Analysis (FMEA) to identify potential failure modes and their effects