Aionix offers a customizable Industry 4.0 platform that integrates AI‑powered computer‑vision inspection with IIoT sensor data via OPC‑UA/MQTT into a modular Manufacturing Execution System. The system streams high‑resolution images and machine metrics to a unified data lake, computes real‑time OEE, scrap and downtime metrics, and provides dashboards and predictive‑maintenance alerts for operators. It can be linked to existing ERP or SCADA systems for mid‑size to large manufacturers.
Funding
Funding not disclosed
Founders
Product
Problem
Manufacturers often rely on manual quality checks and disconnected machine data, resulting in delayed defect detection, low equipment utilization, and limited visibility into production performance. Without integrated real‑time monitoring, optimizing throughput, reducing waste, and meeting sustainability targets becomes difficult.
Solution
Aionix delivers a bespoke Industry 4.0 platform that fuses computer‑vision inspection with IIoT data acquisition and a modular Manufacturing Execution System (MES). Edge devices capture high‑resolution images for AI‑driven defect detection while sensors stream machine metrics via OPC‑UA/MQTT to a unified data lake. The MES layer normalizes this data, calculates OEE, and presents actionable dashboards that enable operators to intervene instantly. Built‑in analytics generate automated alerts for quality deviations and predictive maintenance opportunities. The solution is delivered as custom software tailored to each plant’s equipment mix and workflow, ensuring seamless integration with existing ERP or SCADA systems.
Target Audience
The primary customers are mid‑size to large manufacturers—and progressive SMEs—seeking to digitize production lines, improve quality control, and gain real‑time operational insight across discrete or continuous processes.
Features
- AI‑powered computer‑vision pipeline that performs inline defect classification with sub‑millimeter accuracy.
- IIoT gateway architecture supporting OPC‑UA, MQTT, and Modbus for real‑time sensor ingestion from PLCs and edge controllers.
- Unified data model and cloud‑native analytics engine that computes OEE, scrap rates, and downtime metrics in real time.
- Configurable MES workflow engine that orchestrates work orders, batch tracking, and operator instructions.
- Predictive maintenance module leveraging machine‑learning models on vibration, temperature, and usage data to forecast failures.
- Role‑based web portal with drill‑down dashboards, KPI visualizations, and export APIs for ERP integration.
- Secure, ISO‑27001‑aligned data handling with end‑to‑end encryption and audit logging.