Industrial AI Operating Platform

Cubeon

Industrial AI Operating Platform

Cubeon

Connect data, AI, and operational execution in one platform— from decisions and approval through action and verification.

Overview

Connect data and AI to the way your organization operates

Cubeon manages data connections, AI models and agents, approvals, execution, and result verification in one platform.

Data and system connectivity

Connect equipment, sensors, business systems, documents, and video through standard interfaces

A shared vocabulary

Different names and formats reconciled under shared terms and relationships, so context and meaning line up

Model and agent operations

Manage training, deployment, performance, and access for analytical models and specialist agents

Approval, action, verification

Review evidence, approve actions, execute safely, and verify results and history

Features

Six core modules connecting data to execution

Operate knowledge and AI services on a shared data foundation, then execute safely through your approval process.

Data Foundation

Unified data management

Connect structured, unstructured, and industrial data and prepare it for reliable AI use.

Source connections: Standard connectors for systems, equipment, documents, APIs, and sensors
Data pipelines: Connect → parse → cleanse → standardize → create datasets
Quality checks: Continuously track completeness, consistency, freshness, and validity

Model Operations

AI model operations

Manage the model lifecycle from training and validation through deployment, monitoring, and retraining.

Model registry: Manage versions, training data, and performance metrics together
Deployment: Release validated models safely into operating environments
Monitoring: Continuously check data change and prediction quality

Workflow Orchestration

Workflow orchestration

Coordinate agents and people in a defined sequence to complete one operational workflow.

Decision synthesis: Combine agent decisions and recommendations into one flow
Approval routing: Route approvals automatically according to risk policy
Execution: Send approved results to operating systems

Knowledge Intelligence

Ontology-based knowledge

Connect meaning, relationships, and context to build an enterprise knowledge system.

Knowledge graph: Explore classes, entities, and relationships as a graph
Semantic model: Manage ontology schemas and property mappings
Revision history: Manage ontology versions and domain classifications

Agent Operations

Specialist AI agents

Answer questions, analyze documents, and perform tasks within defined permissions.

Grounded answers: Respond using retrieved documents and data as evidence
Access awareness: Separate knowledge and capabilities by role
Tool connections: Call business systems and AI tools safely

Governance Console

Unified monitoring and audit

Manage detection, decisions, approvals, execution status, and change history in one console.

Operations: Track service state, response time, and errors together
Audit history: Record models, knowledge, and every execution step
Recovery: Restore a verified previous version when problems occur

Architecture

Combine the functions you need with eight shared modules

Start with the modules and domain capabilities you need, then expand the same operating foundation step by step.

Connect

Connects sources that have little in common — controllers (PLC), sensors, manufacturing execution (MES) and enterprise resource planning (ERP).

Edge collection and recovery — Data held locally when the network drops, sent again once it is back
Secure connections — Encryption, device authentication and role-based access protect data in transit

Industrial protocolsIntegration APIs

Fabric

Streaming and scheduled data alike, stored, cleansed and delivered without drama.

Operational visibility — Latency and error rates measured at every stage, from collection to control, to find where it hurts
Scaling in steps — The same proven structure reused, from one line to the whole company

StreamingBatch

Semantic

A shared data format and an ontology of terms and relationships bring names and meanings into line.

Consistent quality — One format, quality checks and gap filling leave the data fit to analyse
Search by relationship — Follow the links between data, documents and work to find what you need

Industry terms and relationships (ontology)

ModelOps

Training, validation and deployment history are kept, accuracy is watched in service, and the model is retrained when it needs to be.

Detecting change — Shifts in input data and predictions signal when to retrain
Audit trail — Every judgement and action recorded, ready for quality and regulatory questions

Train, validate, deploy, retrain

Agents

Agents that answer questions, read documents and get work done.

Grounded answers — Answers built on the documents and data found, with the sources kept
Permission aware — Role-based access decides which knowledge and functions each person can reach

Retrieval-augmented generation (RAG)In-house small language model (sLLM)

Orchestrator

Several agents, each with its role and turn, brought together to finish one piece of work.

Bringing judgements together — What each agent concludes and recommends, merged into a single flow
Approval branching — Risk and reversibility decide which approval path an action takes

AI tool and system integration

Console

Detection, judgement and execution, managed on one screen.

One operations dashboard — Service health, response time, throughput, errors and work in progress, all in one view
Change and rollback — Changes to models, knowledge and settings are recorded, and an earlier version can be restored

Copilot

Choose what answers the questions — a commercial large language model, or a small one running in-house.

Deployment choice — A commercial AI service, an in-house build, or a network cut off from the internet — whichever the rules require
A fixed data boundary — Policy fixes where restricted data is processed, so it never crosses the line

Product Structure

Extend domain capabilities on a shared core

Reuse the common foundation for data, AI, and execution, then add the domain packs and copilots required for each challenge.

Shared operating foundation

Horizontal Core

Combine eight modules from Connect through Console to establish shared standards for data, AI operations, approval, and execution.

Manufacturing domain pack

Manufacturing Pack

Apply anomaly detection, predictive maintenance, quality prediction, process optimization, and energy optimization to manufacturing.

Safety domain pack

Safety Pack

Analyze video, sensors, and operational events together to identify risk and provide evidence and response priorities.

Knowledge service

Copilot

Answer questions from enterprise documents and data, explain results, and support the follow-up work that people need.

Operating Levels

Automate gradually within verified boundaries

Start with detection and alerts, add human approval, and expand toward controlled automated execution.

Detect

Collect data, understand state, detect anomalies, and alert

Decide

Analyze causes, recommend action, and obtain approval

Act

Execute within approved boundaries, verify, and improve

Deployment

Deploy to meet data and security requirements

Choose edge processing, on-premise deployment, or a hybrid model connecting private and cloud systems.

Edge

Process near equipment to minimize latency and external data transfer

On-Premise

Operate sensitive data and AI models inside the organization’s security boundary

Hybrid

Divide roles across edge, private systems, and cloud for security and scalability

Ideal Use Cases

It suits organisations like these

For organisations that want scattered data and AI services brought under one operating standard — and carried through to action.

AI initiatives are fragmented across teams

Teams run data and models differently and need shared standards and reusable structures

AI results do not lead to operational action

Analysis exists, but approval, follow-up action, and result verification are not connected

Critical decisions require human confirmation

Safety, quality, and cost decisions require evidence and final approval by accountable people

Data cannot leave the security boundary

Processing and deployment must comply with edge, on-premise, or isolated-network policies