Ai-OPs

Founded by Phillip Hansel in 2019

Deploy real-time machine learning models directly on industrial control systems and edge devices

Pricing model

Demo

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Koios to deploy machine learning models directly on edge devices and industrial control systems in heavy industry. The system connects to existing plant hardware via protocols like OPC-UA, Modbus TCP, and Ethernet/IP without modifying core operations. You can run predictive inference in real time to monitor process stability, reduce energy consumption, and prevent machine failures before they happen. It features built-in workspaces to write custom control logic in Python, inspect operational data trends, and manage hardware connections across air-gapped or on-premise environments.

What Ai-OPs does

Deploy edge inference containers using DockerConnect industrial devices over Modbus TCP or OPC-UAWrite custom Python controllers and data transformsMonitor live process trends on interactive dashboardsExport high-volume time-series data streamsManage custom machine learning model inputs and sample ratesTrack system configuration changes with append-only audit logsRuns real-time machine learning inference at the edgeConnects natively via OPC-UA, Modbus TCP, Ethernet/IP, and serial interfacesSupports air-gapped, on-premise, and containerized deployments via Docker or KubernetesIncludes an isolated Python workspace for custom PID controllers and logicMaintains an append-only audit trail and TLS certificate securityProvides real-time process dashboards and streaming trend data exportsHandles multi-frequency inputs with varying sample rates into a single model