Industry 02
Digital intelligence for industrial operations.
Deploint engineers sensor-to-decision systems for industrial environments: industrial IoT, edge AI, predictive maintenance and digital twins that connect assets and operational technology to the people who run them.
02 / Industrial
System pattern
Engineered for
- OT safety
- Edge latency
- Intermittent networks
- Brownfield assets
Typical system flow
06 stages
- 01Sensors
- 02Edge
- 03AI
- 04Cloud
- 05Digital twin
- 06Operations
Focus areas
- Industrial IoT
- Predictive maintenance
- Digital twins
- Edge AI
- Asset monitoring
01Solutions
From sensor data to operational decisions.
- 01
Industrial IoT
Sensor, PLC and historian data collected over industrial protocols and published through a secure, structured data pipeline.
- 02
Predictive maintenance
Condition models on vibration, thermal, current and process data that rank assets by risk and show the signals behind each recommendation.
- 03
Digital twins
Live models of assets, lines and sites that combine telemetry, maintenance history and engineering data for monitoring and what-if analysis.
- 04
Computer vision
Camera-based monitoring of safety zones, equipment state and process conditions, with inference running close to the camera.
- 05
Edge AI
Models packaged, deployed and updated on edge hardware, with local inference that keeps working when the cloud connection does not.
- 06
Factory automation
Integration between MES, SCADA and enterprise systems that removes manual handoffs, without changing validated control logic.
- 07
Asset monitoring
Condition and performance monitoring across fleets of assets, with alert thresholds, trend views and work-order integration.
- 08
Production analytics
Throughput, downtime and OEE analytics built on consistent event definitions across lines and sites.
- 09
Robotics
Software integration for robotic cells and mobile robots: fleet telemetry, task orchestration and connections to plant systems.
- 10
Supply-chain intelligence
Supplier, inventory and demand signals combined with production data to anticipate shortages and plan around them.
02Use cases
Where industrial AI meets the operation.
01
Predictive maintenance programs
Moving maintenance from fixed intervals toward condition-based planning, starting with the asset classes where failures cost the most.
- Vibration sensors
- Edge gateways
- CMMS
02
Remote operations
Central visibility into distributed sites with live telemetry, alarms and operational context for remote teams.
- SCADA
- Historians
- Operations views
03
Process optimization
Analysis of process parameters against quality and throughput to recommend setpoint ranges for operator review.
- Process data
- ML models
- Operator review
04
Safety monitoring
Vision and sensor-based detection of zone intrusions and abnormal conditions, routed to supervisors as alerts.
- Computer vision
- Edge inference
- Alerting
05
Asset performance management
One view of asset health, maintenance history and performance across sites, linked to the digital twin.
- Digital twin
- Asset registry
- CMMS
03Reference architecture
Sensors to operations, one governed path.
Vibration, temperature, pressure, current and vision sensors, alongside PLC and historian data from existing control systems.
- Vibration
- Thermal
- PLC tags
- Historians
- Cameras
Industrial intelligence architecture
01 / 06
Sensors
Vibration, temperature, pressure, current and vision sensors, alongside PLC and historian data from existing control systems.
- Vibration
- Thermal
- PLC tags
- Historians
- Cameras
04Engineering considerations
Constraints that shape industrial systems.
Constraint 01
Operational safety
Software that touches operational technology must never compromise the safety functions of the plant.
Engineering response
- Read-only integration by default
- Control logic stays with control engineers
- Defined fail-safe behavior when software fails
Constraint 02
OT network security
OT networks were not designed for cloud connectivity. Every new connection is a potential path into the plant.
Engineering response
- Zone and conduit segmentation informed by ISA/IEC 62443
- Outbound-only connections through a DMZ broker
- Certificate-based device identity
Constraint 03
Latency and connectivity
Many decisions cannot wait for a round trip to the cloud, and many sites have constrained or intermittent links.
Engineering response
- Edge inference for time-sensitive decisions
- Store-and-forward buffering
- Bandwidth-aware aggregation
Constraint 04
Brownfield integration
Plants run equipment and control systems that span decades and vendors.
Engineering response
- Protocol adapters for legacy equipment
- Historian integration rather than replacement
- Retrofit sensing where no data exists
Constraint 05
Data quality
Sensor drift, gaps and inconsistent tag naming undermine models before they are trained.
Engineering response
- Asset and tag modeling standards
- Quality checks at ingestion
- Calibration and drift monitoring
Constraint 06
Trust on the floor
Operators and planners act on recommendations only when they understand them.
Engineering response
- Explainable signals behind each alert
- Feedback captured from maintenance outcomes
- Thresholds tuned with operations teams
05Related capabilities
Engineering disciplines behind the work.
- 06
Digital Engineering
Industrial IoT, edge computing, computer vision and digital twins for physical operations.
- IoT
- Edge computing
- Embedded systems
- 05
Data & Machine Learning
Time-series platforms, condition models and MLOps for fleets of industrial assets.
- Lakehouses
- Streaming
- ETL / ELT
- 01
AI & Agentic Engineering
Edge AI, computer vision and operational assistants for plant and maintenance teams.
- Agentic AI
- RAG
- LLM applications
- 03
Cloud & Platform Engineering
Device management, data platforms and deployment pipelines that span edge and cloud.
- Kubernetes
- Terraform
- CI/CD
06Concept architectures
Reference architectures for related problems.
- Concept Architecture
02Industrial
Industrial Predictive Maintenance
Vibration and thermal telemetry processed at the edge, modeled in the cloud and surfaced to maintenance planners as ranked, explainable work recommendations.
View architecture
- Concept Architecture
05Manufacturing
Computer Vision Quality Inspection
Line-side cameras and edge inference that flag surface defects in real time, with operator feedback loops that continuously improve the model.
View architecture
All concept architectures
Browse every reference architecture, from clinical operations to real-time financial intelligence.
View all
07FAQ
Common questions.
01Do you modify PLC or control system logic?
Our default is read-only integration with control systems through historians, OPC UA servers or gateways. Where a project needs to write to control systems, that work is scoped with your control engineers, who keep ownership of control logic and safety functions.
02Can industrial AI run without a reliable cloud connection?
Yes. Edge deployments are designed to keep running inference, alerting and buffering locally during network outages, then synchronize with the cloud when connectivity returns.
03Which industrial protocols do you work with?
Common protocols include OPC UA, Modbus, MQTT and vendor historian interfaces. For equipment without accessible data, we evaluate retrofit sensing. Support for specific equipment is confirmed during discovery.
04How do you start a predictive maintenance program?
With one asset class where failures are costly and data is available. We establish the data pipeline and a baseline, validate models against maintenance records, and only then expand to more assets or sites.
Industrial engineering
Connecting industrial assets to decisions?
Bring us the assets, the data and the operational problem. We'll help architect the system from sensor to operations.