Sparse expert architectures
Activate specialised capacity only when it is needed.
NeuroCluster Labs · Research programme
Building AI that understands how Europe operates.
European Industrial Intelligence
Research programme·In developmentGeneral-purpose AI understands language. Industrial intelligence must understand assets, networks, processes, time, physics, constraints, risk and operational authority.
NeuroCluster Labs is researching a new class of sovereign AI models designed for Europe's physical and critical industries.
Not another chatbot. Models that learn how industrial systems behave.
The research thesis
The next foundation model will not only understand text.
A transmission grid is not a collection of documents. Neither is a factory, railway network, water system, telecommunications network or logistics operation.
They are interconnected systems whose state changes continuously. Industrial AI therefore needs to reason across multiple forms of information at the same time:
Forms of industrial information
- Language
- Time series
- Topology
- Events
- Imagery
- Assets
- Geospatial data
- Physics
- Workflows
European Industrial Intelligence is our research programme to bring these representations together.
From language models to industrial models
One system. Multiple forms of intelligence.
- LanguageTechnical documents, procedures, standards, maintenance records, work orders and operational communication.
- TimeTelemetry, sensor data, measurements, events, trends and changing system state.
- GraphAssets, networks, dependencies, connectivity and operational topology.
- VisionInspection imagery, thermal imagery, satellite imagery, video and equipment observations.
- GeospatialInfrastructure location, environment, weather and geographical relationships.
- PhysicsPhysical constraints, engineering models, simulation results and validated system boundaries.
- Enterprise contextIdentity, policy, responsibility, approvals, workflows and organisational knowledge.
The objective is not to make every modality look like text.
It is to create models that develop a shared representation of the industrial system they are reasoning about.
The model system
European Industrial Intelligence Model
We are researching a modular model architecture rather than assuming one enormous model should solve every problem.
Try a question
Components active
6 / 6
Illustrative routing. The long-term research direction is a mixture-of-experts model in which each question activates only the specialised capacity it needs.
Industrial Reasoning
Reasoning over technical knowledge, operational situations and evidence.
Industrial Time
Models for telemetry, events, forecasting and temporal relationships.
Industrial Graph
Representation learning for assets, networks, dependencies and topology.
Industrial Vision
Multimodal models for inspections, defects and physical infrastructure.
Industrial Agents
Models trained to interact safely with tools, simulations and enterprise systems.
Industrial World Model
Research into shared representations of system state and the potential consequences of interventions.
European Industrial Intelligence Model
Together, these components form the model programme.
Sector layers such as Grid Foundation Models specialise these components for one industry. Grid Foundation Models
Research architecture
Model once. Understand the whole system.
Eight stages, from what the system observes to what it learns from the outcome. Select a stage to see what it covers.
↺ Measured outcomes feed the next cycle
01 — OBSERVE
Take in the signals an industrial system already produces, wherever they live.
- Documents
- Sensors
- Events
- Images
- Networks
- Weather
- Markets
- Enterprise systems
A different training strategy
Industrial knowledge cannot come from the public internet alone.
The programme investigates training and adaptation using a combination of sources — each with its own route into the model.
Open knowledge
Public and structured
- Public European technical datasets
- Engineering and scientific literature
- Standards and structured technical knowledge
- Domain ontologies
- Machine-readable regulations
Generated
Safe to create at scale
- Synthetic industrial environments
- Simulation-generated observations
Operational
Under controlled access
- Anonymised or federated operational datasets
Controlled European environment
- 01Train
- 02Adapt
- 03Evaluate
Customer-controlled private adaptation
Inside the organisation's own boundary. Operational knowledge stays with the organisation.
Sensitive operational data does not need to become part of a central public model.
Models can be trained, adapted and evaluated inside controlled European environments.
Architectural principles
Efficiency before scale.
Frontier capability does not have to mean maximum parameter count. Our research explores:
Domain experts
Specialised model components for energy, industrial systems, infrastructure and engineering.
Multimodal representations
Combine text, graph, time-series, image and geospatial information.
Verifiable rewards
Train reasoning against simulations, calculations, constraints and measurable outcomes where possible.
Synthetic worlds
Generate safe training environments for rare and critical operational situations.
Tool-native reasoning
Use validated solvers, simulators, databases and engineering applications instead of asking the language model to approximate everything itself.
Sovereign deployment
Design models for European cloud, dedicated infrastructure, on-premises and isolated environments.
Industries
One intelligence foundation. Different industrial worlds.
Energy & grids
Networks, constraints, generation, demand, assets and system operation.
Grid Foundation ModelsManufacturing
Production, equipment, quality, supply chains and maintenance.
Mobility & logistics
Networks, vehicles, routes, capacity and disruptions.
Telecommunications
Networks, services, incidents, capacity and infrastructure.
Climate & water
Physical networks, environmental state, forecasting and resilience.
Defence & civil protection
Mission-critical information environments with controlled authority boundaries.
Critical infrastructure
Cross-domain dependencies and infrastructure resilience.
Sovereign by architecture
European intelligence should remain under European control.
The research programme is designed around:
European compute
Training and inference deployable on European infrastructure.
European data boundaries
Data location and access remain explicit.
Open interfaces
No critical application should depend on a single proprietary model.
Inspectable evaluation
Capabilities are benchmarked rather than implied.
Portable models
Models can move between compatible sovereign infrastructures.
Customer-controlled adaptation
Organisations retain control over their operational knowledge.
NeuroCluster Labs
Models are research. Operational intelligence is the outcome.
A model by itself cannot safely operate critical infrastructure. NeuroCluster connects models to the additional layers required for production.
Model capability
- Data
- Ontology
- Models
- Agents
Operational authority
- Policy
- Human authority
- Action
- Evidence
This separates model capability from operational authority.
A model can understand. NeuroCluster determines what it is allowed to do.
NeuroCluster Labs
Research
- Models
- Datasets
- Benchmarks
- Evaluations
- Research
NeuroCluster platform
Production
- Data
- Ontology
- Governance
- Agents
- Evidence
- Runtime
Industrial applications
Consumption
- GridSight — for the energy market
- Industrial operations
- Infrastructure intelligence
- Future domain applications
Build Europe's industrial intelligence with us.
We are looking to collaborate with European industrial organisations, research institutes, universities, infrastructure operators, AI Factories and technology partners.
- Contribute data.
- Contribute compute.
- Contribute research.
- Contribute a real industrial problem.