Most industrial IoT projects fail not because the technology does not work, but because the cost model breaks before the rollout finishes. We build Industrial IoT systems that scale economically — edge-first architectures that keep cloud bills predictable, brownfield integrations that work with the equipment you already own, and operational outcomes (downtime reduction, throughput, energy savings) that finance teams can actually point to in a quarterly review.
What We Offer
We design and ship end-to-end IIoT systems: sensor and PLC integration on the factory floor, edge gateways and protocol bridging, secure cloud ingestion, time-series storage, real-time dashboards, and the ML models that turn raw telemetry into predictive maintenance alerts and process optimization recommendations. We work with greenfield deployments but spend most of our time on brownfield — plants where the newest piece of equipment is from 2008 and the oldest still runs RS-485 serial.
Key Capabilities
- Industrial protocol coverage: MQTT and Sparkplug B, OPC-UA, Modbus TCP/RTU, EtherNet/IP, BACnet, and legacy serial protocols. We bridge whatever is on the floor today to a unified data layer.
- Edge analytics: On-device filtering, aggregation, and inference using NVIDIA Jetson, AWS Greengrass, Azure IoT Edge, or industrial PCs running Docker. You only send the data that matters to the cloud, which keeps egress bills sane.
- Cloud IoT platforms: AWS IoT Core, Azure IoT Hub, and Google Cloud IoT — including device management, OTA firmware updates, certificate-based auth, and large-fleet provisioning.
- Predictive maintenance ML: Anomaly detection, remaining-useful-life models, and vibration/thermal signature analysis using scikit-learn, PyTorch, and the time-series databases that actually scale (TimescaleDB, InfluxDB, ClickHouse).
- Sensor data pipelines: Stream processing with Kafka, AWS Kinesis, or Azure Event Hubs feeding both real-time dashboards and the historical store your data scientists actually want to query.
- Manufacturing dashboards and OEE: Live OEE (Overall Equipment Effectiveness), shift reports, downtime root-cause tagging, and the kind of operator UI that survives a 12-hour shift on a touchscreen in a noisy plant.
Our Process
1. Discovery & Architecture
We start with a site visit. Walking the floor with operations and maintenance leads tells us more in two days than four weeks of remote calls. We audit existing PLCs, sensors, network infrastructure, and SCADA/historian systems, then produce a costed architecture: how many edge gateways, what protocols, what cloud spend at steady state, and where the highest-ROI use case is for phase one.
2. Design & Prototyping
Before committing to a full rollout, we prove the data path with one line, one cell, or one piece of equipment. We instrument it, send data to the cloud, build the dashboard, and validate that operators and engineers find the output useful. This usually takes 4-8 weeks and gives leadership a real artifact — not a slide deck — to make the go/no-go decision on.
3. Development & Integration
Production rollout runs in waves. Each wave adds equipment, sensors, or sites in chunks small enough to debug and large enough to deliver value. We harden the edge software, build the device management and OTA story, set up monitoring and alerting (Grafana, Prometheus, Datadog), and integrate with your MES, ERP, or CMMS so the IIoT data actually flows into operational decisions.
4. Launch & Support
We do not consider a deployment "done" until operators are using it without prompting. We train maintenance and IT staff, document runbooks, and stay on for the first 60-90 days of full production. Most clients keep us on a usage-based retainer for ongoing model retraining, new use cases, and the inevitable "can we add these 200 sensors to the next plant" conversation.
Industries We Serve
- Discrete manufacturing: Automotive parts, electronics assembly, and metal fabrication — OEE, predictive maintenance, and quality analytics tied to specific production lines.
- Process manufacturing: Food and beverage, chemicals, and pharmaceuticals — batch tracking, environmental monitoring, and regulatory reporting (FDA 21 CFR Part 11, GMP).
- Energy and utilities: Remote asset monitoring for oil and gas, solar, wind, and grid infrastructure, often over cellular or satellite backhaul with intermittent connectivity.
- Logistics and warehousing: Asset tracking, cold-chain monitoring, and equipment telematics for distribution centers and last-mile fleets.
- Food and beverage: HACCP-aligned monitoring, cold storage telemetry, and traceability systems that survive both audit and operations.