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.
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 historyFeatures
Six core modules, from data through to action
Knowledge and AI services run on one common data foundation, and act safely within your own approval process.
Unified data management
Structured, unstructured and industrial data across the company is connected and prepared in a form AI can use.
- Source connections
- Core systems, equipment, documents, APIs and sensors through standard connectors
- Cleansing pipeline
- Connect → parse → cleanse → standardise → build the dataset
- Quality checks
- Completeness, consistency, freshness and validity, watched continuously
Knowledge-based AI agent service
Grounded in your knowledge and data, it understands the question and carries through to analysis, reasoning and the work itself.
- Reasoning shown
- Each step visible, from reading the intent to analysing and summarising
- Tool use
- Knowledge search, document lookup, database queries and report generation
- Sources cited
- The knowledge and data used, with how recent it is
Automation and execution
What the AI finds is carried through to lookups, documents, alerts, approvals and the work itself.
- Closed-loop execution
- Search → analyse → recommend → approve → act
- Approval routing
- An approval queue, with priority and overdue warnings
- Outputs
- Purchase orders and reports generated automatically, with a trail of what was done
Knowledge management on an ontology
Meaning, relationships and context are joined into a body of knowledge that belongs to your company.
- Knowledge graph
- Classes, entities and relationships explored as a graph
- Semantic model
- Ontology schema and property mapping
- Revision history
- Ontology versions and domain classifications
AI Model Hub
From cloud LLMs to on-premises LLMs and small language models— connected and operated to suit the work and the security environment.
- Choosing the deployment
- Cloud, private or on-premises, as the security requirements demand
- Gateway routing
- Models assigned per task, with latency and throughput managed
- Model governance
- Approved models, access limits and audit logs in one place
Security, permissions and operations
Data permissions, agent permissions, logs, and model and knowledge versions— all managed together.
- Three layers of permission
- Data, agent and model permissions managed by role
- Audit and monitoring
- Logs of lookups, actions and permission changes, with security events tracked
- Version control
- Knowledge and model versions, with a record of policy approvals
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
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
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
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
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
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
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 scalabilityIdeal 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.