Senlytics provides advanced sensor data analytics for industrial asset monitoring and predictive maintenance. The platform integrates diverse IoT sensor streams to deliver real-time operational insights and anomaly detection. This enables clients to optimize equipment uptime and reduce unplanned maintenance costs through data-driven decision-making.
Funding
Funding not disclosed
Founders
Product
Problem
Manufacturers often operate with a fragmented ecosystem of IoT sensors that use different protocols, data formats, and sampling rates. This heterogeneity makes it difficult to aggregate real‑time measurements, leading to delayed detection of equipment anomalies and costly unplanned downtime.
Solution
Senlytics delivers a cloud‑native analytics platform that continuously ingests and normalizes heterogeneous sensor streams from the shop floor. Scalable processing pipelines apply edge‑level filtering and machine‑learning models to generate real‑time operational metrics, anomaly scores, and predictive maintenance alerts. The platform exposes a unified data schema and REST/GraphQL APIs that integrate directly with existing MES, ERP, and SCADA systems. Users can build custom KPI dashboards, configure threshold‑based notifications, and drill down to root‑cause diagnostics without writing code. For latency‑critical sites, Senlytics offers on‑premise edge agents that mirror the cloud analytics while receiving centralized model updates. The overall workflow enables data‑driven decision making that shortens mean time to repair (MTTR) and improves overall equipment effectiveness (OEE).
Target Audience
The primary customers are manufacturers and plant operators in process, discrete, and heavy‑industry sectors who need to consolidate sensor data for real‑time monitoring and predictive maintenance, as well as industrial IoT solution providers integrating analytics into their offerings.
Features
- Multi‑protocol ingestion engine supporting OPC-UA, MQTT, Modbus, and RESTful sensor endpoints
- Real‑time data normalization layer that maps disparate payloads to a common asset model
- Edge analytics runtime for low‑latency filtering, feature extraction, and local anomaly detection
- Pre‑trained and customizable machine‑learning models for vibration, temperature, and pressure‑based predictive maintenance
- Drag‑and‑drop KPI dashboard builder with built‑in visualizations (trend charts, heat maps, histograms)
- Secure API gateway with OAuth2, role‑based access control, and end‑to‑end TLS encryption
- Horizontal scaling via containerized microservices orchestrated on Kubernetes for high‑throughput environments
- Integration adapters for major MES/ERP platforms (Siemens Opcenter, SAP PP, Rockwell Automation) and standard OPC‑DA/UA bridges