Where physical sensors meet intelligent inference. We build the telemetry pipelines, edge AI models, and digital twins that turn a fleet of devices into a system that thinks, safely, reliably, at scale.
A connected device that just streams telemetry to a cloud bucket isn't IoT, it's a phone bill. Real IoT and AI integration is sensors that know what they're seeing, edge nodes that decide locally when the cloud is slow or absent, and a fleet management layer that lets you operate thousands of devices without sending a technician to each one.
We build the entire stack: device connectivity (MQTT, OPC-UA, LoRaWAN, BLE, cellular), edge AI inference (TensorFlow Lite, ONNX Runtime, NVIDIA Jetson), predictive maintenance models, digital twins, computer vision at the edge, and the cloud to edge orchestration that ties it all together. From a single sensor cell to a continent wide fleet, the patterns are the same, we just calibrate the depth.
Every system we ship is designed for the realities of the field: intermittent connectivity, constrained compute, harsh environments, OTA updates, security at the device, and the regulatory reality of operating in regulated sectors. The goal isn't a demo, it's a fleet that runs for years.
A complete stack, from sensor wire to cloud dashboard, with intelligence baked into every layer.
Every sensor, gateway, controller, and PLC speaks a different protocol. We stitch them together with MQTT, OPC-UA, Modbus, LoRaWAN, and cellular bridges, then route the resulting stream into a time series store that scales to millions of messages per second.
Models that run on the device, not in the cloud. We compress, quantize, and deploy production ML to Jetson, Coral, Raspberry Pi, and custom silicon, with latency under 50 ms, no round trip, and graceful degradation when the network drops.
Multimodal sensor fusion (vibration, temperature, current, acoustic) feeding survival models that predict failure hours before it happens. Plus remaining useful life estimation that schedules maintenance windows around production, not against them.
Live, calibrated digital twins of pumps, lines, vehicles, buildings, and entire facilities, for scenario simulation, anomaly detection against expected behavior, and operator training without taking the real asset offline.
Defect detection, safety compliance, occupancy, asset tracking, with vision models running on the camera or on a nearby edge box. Privacy preserving by design: in regulated environments we never let raw video leave the perimeter.
Staged rollouts, canary deployments, rollback on regression, certificate rotation, telemetry driven health checks, the operations layer that keeps a fleet of thousands of devices on a current, trusted firmware without sending a truck.
Three representative deployments, the physical environment, the data, and the decisions our integration unlocked.
A discrete manufacturer instrumented its production lines with mixed brownfield PLCs and new wireless sensors. We unified the streams in a single time series pipeline, deployed a per line anomaly model on edge boxes next to the cells, and built an operator dashboard that flags slow burn defect patterns before the QA gate catches them.
A long haul fleet operator was running calendar based maintenance and still seeing roadside breakdowns. We pulled engine, transmission, and brake telemetry into a survival model that predicts failure days in advance, scheduling each truck through the right yard at the right time, not when the calendar said.
A property operator wanted to cut HVAC costs without sacrificing occupant comfort. We layered occupancy sensing, weather forecasts, and tariff signals into a control loop that precools the building when energy is cheap and rides through peak hours on stored thermal mass, with a twin that proves the savings.
IoT projects fail for predictable reasons: cloud shaped designs in places with no cloud, security as an afterthought, fleets that can't be updated. These five rules keep us out of those traps.
A two week discovery sprint. We map your existing fleet, the protocols they speak, the data they're already generating, and the decisions you'd like to automate. The deliverable is a one page architecture proposal and a phased rollout plan, not a sales deck.
Yes. We bridge brownfield equipment via OPC-UA, Modbus TCP/RTU, BACnet, and proprietary protocols where vendors permit it. The data lands in a normalized time series store regardless of where it came from.
Yes, that's most of what IoT AI actually requires. We deploy on NVIDIA Jetson, Google Coral, x86 industrial gateways, and custom silicon, with optional cloud sync when connectivity is available. Inference happens locally; the cloud is for fleet management and offline analytics.
We build for that from the start. The same telemetry pipeline that handles 10 devices in your lab handles 10,000 in the field, because the architecture (MQTT broker, time series store, fleet management plane) doesn't change. What does change is the operational tooling: OTA updates, certificate rotation, anomaly detection on the fleet itself.
Hardware rooted identity (TPM where the device supports it), mutual TLS for every connection, signed firmware with a verified boot chain, OTA update channels with rollback, and a published vulnerability disclosure path. Security is baked in at design time, not bolted on later.
Tell us the devices, the environment, and the decision you want pushed to the edge. We'll come back with an architecture sketch and a realistic plan for getting your first AI powered devices into the field.