Ai-OPs

Founded by Phillip Hansel in 2019

Run AI models on industrial control systems at the edge in real time

Cost

Demo

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Ai-OPs to deploy AI inference directly on your existing industrial control systems without sending data to the cloud. It connects to equipment via OPC-UA, Modbus, Ethernet/IP, and other protocols, then runs predictive models on live process data to reduce energy use, prevent failures, and stabilize operations. You can build custom controllers in Python, view real-time dashboards, and manage historical process data — all within an air-gapped or on-premises environment.

What Ai-OPs does

Pull and deploy the Koios Docker image to an existing control systemConnect industrial equipment via OPC-UA, Modbus, or Ethernet/IPRun predictive AI models on live process sensor dataBuild custom Python-based controllers and deploy them as hot-reloadable packagesView real-time process dashboards and historical trending dataConfigure role-based access and audit trails for plant-floor usersMonitor model performance and manage deployed AI models from a central interfaceSet up air-gapped deployment so no data leaves the facility networkRuns AI inference directly on existing industrial control hardware without cloud dependencyConnects to equipment using OPC-UA, Modbus TCP, Ethernet/IP, SOAP, SQL, and REST out of the boxSupports fully air-gapped deployments where data never leaves the facilityDeploy via Docker or Kubernetes in under five minutes using a single image pullBuild custom PID controllers, state machines, and data transforms in Python as hot-reloadable packagesReal-time dashboards and historical trending built specifically for control engineersRole-based access control, encrypted connections, and full audit trails for enterprise securityFirst company to implement direct process control using reinforcement learning in a live environment

Frequently asked

Docker

Kubernetes

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