An AI Stack Designed
Around the Operation.
Industrial environments demand architecture decisions that generic AI tooling does not make: data ownership, isolation, traceability and integration with systems already running production.
Layer by Layer.
Applications
- Engineering Copilot
- Plant Knowledge Assistant
- Predictive Quality
- Process Optimization
- Root Cause Analysis
- Workflow Automation
Agents
- Engineering agents
- Research agents
- Quality agents
- Maintenance agents
- Operational agents
Intelligence
- LLMs
- Machine Learning
- Computer Vision
Knowledge Infrastructure
- Ingestion
- Embeddings
- Vector search
- RAG
- Reranking
- Knowledge graphs
Knowledge
- Manuals
- SOPs
- Engineering documents
- Specifications
- Historical information
- Operational knowledge
Industrial Systems
- PLC
- Sensors
- SCADA
- MES
- ERP
- Industrial databases
Your Factory Knowledge
Stays in Your Environment.
Where operational or confidentiality requirements demand it, models and retrieval run inside infrastructure the customer controls.
- Private cloud
- Customer-controlled infrastructure
- Local GPU systems
- On-premise models
- Controlled APIs
- Private RAG environments
Industrial Data
PLC · SCADA · MES · Quality
Engineering Knowledge
Manuals · SOPs · Drawings
Private AI
Local GPU · On-premise models
Applications
Copilots · Agents · Analytics
Engineering Discipline.
Explicit Layers
Data, retrieval, models, agents and applications stay separable, so each can be validated, replaced or audited independently.
Traceable Answers
Industrial retrieval returns sources. Engineers can see which document, record or signal produced a recommendation.
Operational Fit
Latency, uptime, network isolation and integration with existing industrial systems are design inputs, not afterthoughts.