Real-time visibility from
sensor to business system.
IIoT infrastructure connecting plant-floor data to operational intelligence — OEE tracking, edge computing, and dashboards designed around the decisions your teams actually make.
Data trapped in PLCs, overwritten every scan. Historians nobody queries. No connection between production and business systems.
- —Manual OEE tracking on whiteboards
- —No visibility into energy consumption
- —Reactive maintenance only
Real-time visibility from sensor to business system. Data that drives decisions, not meetings.
- Automated OEE with drill-down analytics
- Energy cost per unit produced
- Condition-based predictive maintenance
Not more data,
better decisions.
Your plant generates enormous amounts of data every second. Temperature readings, pressure measurements, motor currents, cycle counts, vibration signatures, energy consumption — all of it streaming from sensors, PLCs, and controllers across your floor. Most of it goes nowhere.
The problem isn't data collection. Most modern equipment already generates the data you need. The problem is getting that data out of the control layer, contextualizing it with production information, and putting it in front of the people who can act on it — in a format that drives a decision, not a meeting about what the data might mean.
Not a generic analytics platform with a manufacturing skin — a system engineered for your equipment, your process, and your operational structure.
Six layers of
operational intelligence.
IIoT Architecture Design
End-to-end architecture from sensor to dashboard. We design the data path — what gets collected, where it's processed, how it moves, where it's stored, and who consumes it. Edge devices for local processing and buffering. MQTT brokers for lightweight, publish-subscribe data transport. Cloud or on-premise historians for long-term storage. Architecture decisions driven by your constraints: network bandwidth, cybersecurity policies, data residency requirements, and latency tolerance.
Edge Computing and Gateway Deployment
Edge devices deployed at or near the equipment to collect, pre-process, filter, and forward data. Protocol translation between legacy equipment (Modbus RTU, serial, proprietary protocols) and modern IIoT infrastructure (MQTT, OPC UA, REST APIs). Data buffering for store-and-forward when network connectivity is intermittent. We work with industrial edge platforms — Ignition Edge, AWS IoT Greengrass, Azure IoT Edge, Moxa, Red Lion — selected for the application and your existing infrastructure.
OEE and Production Performance Tracking
Automated Overall Equipment Effectiveness tracking built on actual machine data — not operator-entered reason codes. Availability from PLC run/stop signals. Performance from actual versus ideal cycle times. Quality from inspection results or operator-confirmed reject counts. Downtime categorization that's useful, with drill-down to specific fault codes. Pareto analysis showing where to focus improvement efforts.
Operational Dashboards
Dashboards designed for specific audiences — the plant manager, line supervisor, maintenance lead, and process engineer all need different views. Floor-level dashboards showing real-time machine status, production count versus target, active alarms, and quality metrics. Management dashboards showing daily, weekly, and monthly trends accessible via web browser. Every dashboard element has a purpose. If a chart doesn't drive a decision, it doesn't belong on the screen.
Energy Monitoring and Optimization
Sub-metering, power monitoring, and energy analytics that connect consumption data to production context. kWh per unit produced. Energy cost per shift. Demand peak identification. Utility data collected from power meters, flow meters, and BTU meters — integrated into the same analytics platform as your production data so energy conversations happen alongside productivity conversations.
Predictive Maintenance and Condition Monitoring
Continuous monitoring of equipment health indicators — vibration, temperature, motor current, bearing condition, pressure differential, and runtime hours. Threshold-based alarms for immediate issues. Trend-based detection for slowly degrading conditions. ML-based anomaly detection where the data and process justify it. Not every machine needs a machine learning model — most benefit from well-designed threshold monitoring and trend analysis.
Every operation
that generates data.
If your equipment generates data that could inform better decisions — and it does — we can build the infrastructure to make that happen.
Multi-Line Manufacturing
OEE tracking, downtime analysis, and production dashboards across multiple production lines with different equipment types and vintages. Unified visibility despite a heterogeneous equipment base.
Process Industries
Continuous process monitoring, energy optimization, and SPC trending for chemical, food and beverage, pharmaceutical, and pulp and paper operations.
Distributed Assets
Remote monitoring for pump stations, compressor stations, well pads, substations, and water systems. Edge computing and store-and-forward architectures for sites with limited connectivity.
Energy-Intensive Operations
Energy monitoring and optimization for facilities where utility costs are a significant portion of operating expense. Sub-metering, demand management, and consumption analytics.
Fleet and Mobile Equipment
Condition monitoring and utilization tracking for mobile assets — forklifts, AGVs, cranes, and heavy equipment. GPS, runtime, and equipment health data collected and centralized.
Brownfield Digitalization
Extracting data from legacy equipment that wasn't designed to be connected. Retrofitting sensors, deploying edge gateways, and building the data infrastructure to bring older assets into a modern analytics framework.
From assessment
to operational insight.
Assess the data landscape.
What data already exists in your PLCs, historians, and controllers? What additional sensors are needed? What are the connectivity options? What are the IT/OT governance constraints? We map the current state and identify the gaps before proposing an architecture.
Define the use cases.
Not 'we'll collect everything and figure out what to do with it later.' We define the specific operational questions the system will answer and design the data collection and analytics to answer those questions.
Design the architecture.
Edge devices, communication protocols, data transport, historian configuration, cloud infrastructure, and visualization layer. Architecture documented and reviewed with your operations and IT teams. Security and network considerations addressed before deployment.
Deploy and integrate.
Edge hardware installation, gateway configuration, protocol setup, historian tag configuration, dashboard development, and integration testing. Phased deployment — typically starting with a pilot line, validating data quality and dashboard utility, then scaling.
Validate and refine.
Data quality verification against known production records. Dashboard review with the operators, supervisors, and managers who will use them. Adjustments to layouts, calculations, and alarm thresholds based on real feedback from real users.
Scale and sustain.
Expand to additional lines, areas, or facilities. Add analytics layers — predictive maintenance, energy optimization, SPC — once the foundational data infrastructure is proven and trusted. Ongoing support to maintain data quality and adapt to process changes.
Platforms we build on.
Proven industrial platforms with long-term vendor support and large integrator ecosystems — not startup tools that might not exist in three years.
Controls engineers
building data infrastructure.
We start with the question, not the data.
Most IIoT projects start by connecting everything and collecting everything — then figuring out what to do with it later. That approach produces terabytes of data and zero actionable insight. We start with the specific operational questions you need answered and design the data collection, processing, and visualization to answer those questions. Every tag we collect has a purpose.
Controls engineers building data infrastructure.
We understand the data at the source — PLC register structures, historian compression algorithms, scan rate implications, and the difference between a real zero and a communication failure. Most IT-centric IIoT vendors treat plant-floor data as a generic feed. We know what the data means because we've programmed the controllers that generate it.
Built on proven industrial platforms.
We don't build IIoT systems on startup platforms that might not exist in three years. We build on Ignition, established historians, and enterprise cloud providers — platforms with long-term vendor support, large integrator ecosystems, and proven reliability in industrial environments. Your IIoT investment should last a decade, not a funding cycle.
Data quality as a first-class concern.
Clean, reliable data infrastructure serves as the foundation for everything. Historian configuration with appropriate tag organization, scan rates, compression settings, and retention policies. Data contextualization linking raw time-series data to production events, batch IDs, and equipment identifiers. Data quality monitoring to detect sensor failures and stale values before they corrupt your analytics.
Straight answers.
Almost never. We extract data from existing controllers using native communication protocols — EtherNet/IP, PROFINET, Modbus, OPC UA — without modifying the control program. For older equipment without network connectivity, we add edge devices or retrofit sensors that operate independently of the existing control system.
It depends on your use case, data volume, IT policy, and latency requirements. Real-time operator dashboards need low-latency, on-premise infrastructure. Long-term analytics, cross-site comparisons, and ML workloads often benefit from cloud scalability. Many of our deployments are hybrid — on-premise for operations, cloud for analytics and long-term storage. We design the architecture that fits your situation.
IIoT increases the attack surface of your OT network. We design with network segmentation, industrial DMZs, encrypted communication (TLS/SSL), authenticated connections, and read-only data extraction from the control layer where possible. The analytics infrastructure never has write access to your control system. We work within your IT security framework and support IEC 62443 aligned architectures.
SCADA is a real-time control and monitoring system — operators use it to run the process. IIoT analytics sits alongside SCADA and focuses on historical analysis, trending, cross-system correlation, and business-level reporting. They serve different audiences and different time horizons. In many of our deployments, the IIoT layer pulls data from the SCADA system and adds the contextualization, storage, and analytics layers that SCADA wasn't designed to provide.
A focused pilot deployment — one line, OEE tracking, basic dashboards — can be live in 4–8 weeks. Broader deployments across multiple lines with energy monitoring, condition monitoring, and business system integration typically take 3–6 months. We structure projects so the first phase delivers usable insights quickly, and subsequent phases add depth and breadth.
Ready for visibility into your operation?
Whether it's OEE tracking, energy monitoring, predictive maintenance, or connecting legacy equipment to a modern data infrastructure — tell us what you want to see.