Technology
Grounded, agentic, measurable.
AI Architecture
Grounded AI for industrial workflows.
Retrieval and context engineering decide what the model sees. Claude reasons over that context, agents orchestrate tools, and people approve what happens next. Memory, grounding, evaluation and observability run across every layer.
- Scoped Memory
- Grounding
- Evaluations
- Observability
Inputs
- Engineering Drawings
- Machine Manuals
- Sensor Features
- Maintenance Records
- Operational Data
Retrieval + Context Engineering
Select, rank and assemble the right sources
Claude
Reasoning layer
Agent Orchestration
Plan multi-step work across specialist agents
Tool Use
- Engineering Tools
- Maintenance Systems
- Industrial APIs
- Business Systems
Human Approval
Required before important actions
Action
Executed through existing systems, fully traced
Built with Claude
Building industrial intelligence with Claude.
Claude serves as a reasoning layer within our Industrial AI architecture.
We are exploring how Claude can combine retrieved engineering knowledge, machine context and controlled tool access to support complex industrial workflows.
Context engineering
Build relevant context from machines, drawings, users and workflows.
RAG
Retrieve relevant technical documentation and engineering knowledge.
Grounding
Tie AI responses back to source documents and machine information.
Agentic workflows
Coordinate multi-step engineering and maintenance workflows.
Tool use
Allow agents to interact with controlled engineering and operational tools.
Scoped memory
Maintain appropriate machine and workflow context across interactions.
Evaluation
Test groundedness, citations and tool choices before workflows ship.
Observability
Trace every retrieval, tool call and decision for review.
AI-native from day one
We build the platform itself with AI. Our engineering work runs on Claude Code agents for implementation, review and testing, under written guardrails: one change per pull request, tests that can fail, and a human decision at every open design question.
Evaluation
Industrial AI needs to be measurable.
Before AI-assisted workflows can be trusted in industrial environments, their behavior needs to be evaluated systematically.
| Dimension | Status |
|---|---|
| Groundedness | Testing |
| Citation quality | Testing |
| Tool selection | Testing |
| Context retrieval | Testing |
| Task completion | Testing |
Test Cases
Questions and tasks with expected sources and tools
Evaluation Runs
Repeatable runs against each model and prompt version
Agent Traces
Step-by-step record of planning and decisions
Tool Calls
Every call, its arguments and its result
Source References
Which documents and signals supported each answer
- plan
Investigate abnormal vibration on M-04 - tool
sensor_history(M-04, vibration, 24h) - tool
signal_features(M-04) - tool
docs.search("M-04 manual bearings") - source
Motor M-04 Manual · §6 Bearings - check
Every claim cites a source
Safety & human control
AI assistance with engineers in control.
Grounded outputs
Connect AI responses to engineering sources.
Human approval
Require approval before important actions are executed.
Traceable reasoning
Record retrieval, tool calls and workflow execution.
Permission-aware access
Only provide agents with authorized context and tools.
The platform is designed as engineering decision support. AI-generated analysis should be reviewed by qualified personnel before safety-critical decisions are made.
Interested in a technology partnership?