Nanoprecise provides an AI‑driven predictive maintenance platform that continuously ingests sensor data from industrial equipment to forecast component failures and generate risk scores. The solution delivers real‑time alerts and prescriptive maintenance recommendations via an interactive dashboard, integrating with existing SCADA, ERP, and IoT systems to enable condition‑based interventions and reduce unplanned downtime.
Funding
$38M 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.
2OBDYPFounders
Product
Problem
Industrial equipment failures and unplanned downtime lead to high maintenance costs and production losses, especially for organizations with large, distributed asset fleets. Traditional maintenance schedules rely on fixed intervals or manual inspections, which are inefficient and often miss early signs of degradation.
Solution
Nanoprecise offers an AI-driven predictive maintenance platform that continuously analyzes sensor data from machinery to forecast component failures before they occur. The system ingests real-time operational metrics, applies machine-learning models trained on historical failure patterns, and generates risk scores for each asset. Users receive actionable alerts and maintenance recommendations through an intuitive dashboard, enabling condition-based interventions that reduce unnecessary servicing and prevent costly breakdowns. The platform is built to scale across thousands of assets and integrates with existing SCADA, ERP, and IoT infrastructures, allowing enterprises to extend predictive capabilities without extensive reengineering.
Target Audience
Primary customers are manufacturers, energy producers, and large-scale facilities that operate extensive fleets of critical machinery and seek to shift from reactive to condition-based maintenance.
Features
- Real-time data pipeline that collects and normalizes sensor streams from diverse equipment types
- Proprietary machine-learning models for anomaly detection, remaining useful life estimation, and failure mode classification
- Scalable cloud architecture supporting millions of data points per day and multi-site deployments
- Interactive dashboard with asset health visualizations, risk scoring, and prescriptive maintenance actions
- Open APIs and pre-built connectors for seamless integration with SCADA systems, ERP platforms, and edge gateways
- Automated model retraining using continuous feedback loops to improve prediction accuracy over time