Sensight provides a cloud‑native predictive maintenance platform that ingests heterogeneous sensor streams (OPC‑UA, MQTT, Modbus, REST) and applies machine‑learning models to deliver real‑time equipment health scores, remaining‑useful‑life forecasts, and automated work‑order recommendations. The solution includes interactive dashboards, bi‑directional APIs for CMMS/ERP integration, and optional edge‑compute deployment for low‑latency inference. It targets manufacturers, utilities, oil & gas, and mining operators seeking to reduce unplanned downtime and maintenance costs.
Funding
Funding not disclosed
Founders
Product
Problem
Industrial equipment failures lead to unplanned downtime, elevated maintenance expenses, and reduced operational throughput. Many asset-intensive organizations collect large volumes of sensor data but lack the analytics needed to turn those streams into actionable maintenance insights. Consequently, maintenance decisions remain reactive rather than predictive, increasing risk and cost.
Solution
Sensight offers a cloud-native predictive maintenance platform that applies advanced machine learning algorithms to continuous sensor data streams from industrial assets. The system automatically cleans, normalizes, and aligns heterogeneous data sources before feeding them into forecasting models that estimate remaining useful life and failure probability. By delivering real-time risk scores and maintenance recommendations, the platform enables operators to schedule interventions proactively, minimizing unexpected outages. Integrated dashboards provide KPI visualizations and root-cause analytics, while APIs allow seamless connection to existing CMMS and ERP systems. The solution scales across multiple sites and equipment types, supporting both batch and edge‑deployed inference for low-latency use cases. Overall, Sensight helps enterprises shift from reactive repairs to data‑driven asset optimization, reducing downtime and maintenance spend.
Target Audience
Primary customers are asset‑intensive enterprises such as manufacturers, energy utilities, oil & gas operators, and mining firms that manage large fleets of critical equipment and seek to reduce unplanned downtime through predictive analytics.
Features
- Automated data ingestion pipeline supporting OPC-UA, MQTT, Modbus, and RESTful sensor feeds with schema‑agnostic normalization
- Real-time anomaly detection and remaining‑useful‑life (RUL) models built on deep learning and gradient‑boosted trees
- Predictive work‑order generation with priority scoring and recommended spare‑part inventory levels
- Interactive KPI dashboard offering equipment health heatmaps, trend analysis, and drill‑down root‑cause diagnostics
- Bi‑directional APIs for integration with leading CMMS (e.g., IBM Maximo, SAP PM) and ERP platforms
- Edge‑compute deployment option for latency‑sensitive environments, enabling on‑site inference without constant cloud connectivity
- Scalable multi‑tenant architecture with role‑based access control and audit logging for enterprise security compliance