Technology
The ORELSIS technology stack and architecture: C++, Go, Python, cloud, Edge AI and machine learning.
Six layers from shop floor to decision
Data originates on the shop floor, is enriched at every layer and reaches the decision screen. Each layer scales independently and can be replaced.
- L6
Infrastructure
Container-based deployment: on-premise, cloud or hybrid.
Docker · Kubernetes · Cloud - L5
Applications
Web, desktop and mobile interfaces; role-based dashboards and reports.
TypeScript · React · Mobile - L4
AI
Model training and MLOps for forecasting, anomaly detection, optimisation and document intelligence.
Python · PyTorch · TensorFlow - L3
Data platform
OREL DM: one data layer for time-series, relational and document data.
PostgreSQL · TimescaleDB · Object storage - L2
Integration
Reliable data movement via event streams, an API gateway and ERP connectors.
Kafka · REST · gRPC - L1
Field & edge
Data acquisition from PLCs, sensors, RTLS and machines; edge pre-processing and edge AI inference.
OPC UA · Modbus · MQTT
Mature, open and vendor-neutral
Every technology we use and what we use it for.
Languages
05- C++Real-time field and machine software
- GoHighly concurrent services and gateways
- PythonData science, AI and automation
- TypeScriptWeb apps and admin consoles
- RustSafety-critical, low-level components
Field & edge
04- OPC UAStandard PLC and SCADA communication
- ModbusDirect link to existing devices
- MQTTLightweight IoT messaging
- Edge AIRunning models next to the data
Data
04- PostgreSQLTransactional master data
- TimescaleDBTime-series sensor data
- KafkaEvent streams and integration
- Object storageDocuments, images and archives
AI
04- PyTorchDeep learning and vision
- TensorFlowProduction models
- LLMOn-premise language models (OREL AI)
- MLOpsMonitoring, versioning, retraining
Applications
04- ReactWeb interfaces and dashboards
- React NativeiOS and Android from one codebase
- QtDesktop and operator terminals
- REST / gRPCOpen, documented APIs
Infrastructure
04- DockerPortable, repeatable deployment
- KubernetesScaling on-premise or in the cloud
- CloudCloud, hybrid or fully on-premise
- ObservabilityLogs, metrics and alerts
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