Current context
Inference reads the plant as it is rather than as it was when the export ran.
Industrial AI
Machine learning close to the machines it serves.
Industrial AI reads measurements, equipment structure and history, and its output usually ends up as an instruction to a person or a controller.
A model that reads the plant as it is, and leaves its reasoning on the record.
Models applied to plant data, where the context is equipment and the stakes are physical.
The workload is the same retrieval-and-inference loop as any AI feature, with a different context: equipment identifiers, process windows and maintenance history. PLOMID keeps those in one layer so a model reads the operational truth, and every inference can be written back as a record.
A model that reads the plant as it is, and leaves its reasoning on the record.
Environment
The process
Windows of measurements and equipment state, live from the line.
Time series
Two estates, drawn.
The same shapes, held two ways. The difference is not the storage, it is where the agreement between them lives.
Separate systems Copies kept in step
One layer One plan · one contract
What this workload moves.
Every shape below is a surface of the layer, not a format to be converted into one. They are read and written together.
- Time series Measurements and events in time order
- JSON / documents Documents and nested objects
- Vector Similarity and semantic retrieval
- SQL Records, keys and joins
- Graph Relationships and traversal
How the workload reaches the data.
The models this workload names, the path a request takes through them, and the surfaces that speak to the layer.
Industrial AI · architecture
Workload
The shape of the work itself.
- Observe
- Contextualise
- Infer
- Record
Data models
The models this workload names.
- Time series
- JSON / documents
- Vector
- SQL
- Graph
The layer
One path from a request to the data it names.
- Planning Predicates narrow the work before it runs
- Execution Rows, fields and windows answered together
- Transactions Readers and writers do not block each other
Surfaces
How the workload reaches the layer.
- SQL surface The query language the layer is documented in
- Applications Services and jobs reading and writing as they run
- Analytics & AI clients The same layer, the same access path
The work, stage by stage.
Choose a stage to read what happens there, or a shape to see every stage that handles it. Nothing on the map is a private interface.
PLOMID · Industrial AI observe · contextualise · infer · record Select a stage
Stage
Observe
Process windows and equipment state
Time series
01
Observe
Process windows and equipment state
- Time series
02
Contextualise
Equipment records and history attached
- SQL
- JSON / documents
03
Infer
Models applied to the current context
- Vector
04
Record
The inference written back against the asset
- SQL
- Events
What changes when the data is in one place.
Stated as properties of the system rather than as results we cannot measure for you.
Traceable output
A recommendation can be read against the measurements that produced it.
Structure included
Equipment hierarchy and relationships are part of the context a model receives.
Operational feedback
What operators did about an inference becomes data for the next one.
What this is built on.
Industrial inference is only trusted if it is explainable. These are the parts that make it so.
- Retrieval as an access path → Similarity search arrives as another way into the same data rather than a second copy to keep in step.
- Time-ordered storage → Measurements and events are stored beside the records and documents they describe, so history and current state agree.
- Relationships held as data → Edges expressed directly remove the reconstruction that happens at query time when relationships live in a join.
- One data layer → Rows, documents and time-ordered events live in one system, so a question is asked once instead of once per store.
Workloads that sit beside this one.
Where this shows up.
Derived from the industry pages that reference this solution, so the two directions of the relationship always agree.
- Manufacturing → Line telemetry, machine state, work orders and quality records around a plant.
- Automotive → Vehicle telemetry, build records, connected-fleet data and service history.
- Robotics → Robot state, motion traces, task outcomes and fleet coordination.
- Digital Twins → Asset models, live telemetry, relationships and the queries run against them.
- Autonomous Systems → Sensor streams, perception output, decisions, interventions and logs.