AI + IoT

Enable AIoT with device data your systems can understand

AIoT combines connected sensors with AI-ready data. Device Explorer provides the missing operational layer: supported devices, decoded telemetry, consistent data models, and live context for analytics, automation, and predictive workflows.

Explore supported devices

LoRaWAN sensor 23.4°C / 612 ppm CO2 Normalized as temperature and airQuality.co2

Device Data model AI workflow Action

438+ supported LoRaWAN device records in the catalog

66 manufacturers represented in the supported-device library

LoRaWAN + cellular network paths for mixed IoT fleets

What AIoT needs

AI depends on reliable connected-device context

Artificial intelligence of things, or AIoT, brings AI technologies together with IoT infrastructure so connected systems can sense, learn from live data, and support automated decisions. That only works when device payloads are decoded, labeled, and attached to a stable model.

Reference: Artificial intelligence of things

Collect

Connect supported LoRaWAN and cellular devices, receive telemetry, and keep fleet status visible before the data reaches AI or BI systems.

Model

Transform raw payloads into normalized measurements such as temperature, vibration, occupancy, water flow, voltage, location, and battery state.

Activate

Route modeled data into dashboards, business software, alerting flows, and AI pipelines that detect anomalies, forecast maintenance, or recommend field actions.

Supported devices

Use the existing catalog as the AIoT starting point

Device Explorer's supported-device catalog gives teams a practical starting point for AIoT deployments. Instead of building every parser and schema from zero, teams can onboard compatible sensors, trackers, meters, and gateway-connected assets with business-ready telemetry.

Data model

A practical AIoT data model

The model should preserve device identity, measurement semantics, location, time, quality, and operational context so AI systems can compare signals across manufacturers and use cases.

device: Customer-friendly device profile with network type, location, asset relationship, tags, and operating context.
telemetry: Normalized measurement name, value, unit, timestamp, decoded raw payload, and confidence or quality flags.
asset: The real-world equipment, room, meter, vehicle, zone, or infrastructure element represented by the device.
location: Fixed coordinates, mapped site, building, floor, room, route, or last known mobile position.
state: Battery level, signal quality, online status, alarm state, calibration status, and last-contact health.
workflow: Alert rules, AI features, prediction targets, ticket references, maintenance status, and business-system links.

Example

Milesight AM308 indoor air-quality data, ready for AI

For a device such as the Milesight AM308, Device Explorer turns indoor ambience telemetry into clear, reusable signals. Business teams can monitor comfort and air quality, while AI workflows can compare rooms, detect anomalies, and recommend ventilation or occupancy actions.

Milesight AM308 indoor air quality sensor

Example AI-ready output

device.type: indoor_air_quality_sensor
telemetry.temperature: 23.4 degC
telemetry.humidity: 48 %
telemetry.co2: 612 ppm
telemetry.tvoc: 126 ppb
telemetry.pm25: 8 ug/m3
telemetry.pm10: 14 ug/m3
telemetry.pressure: 101.2 kPa
telemetry.motion: occupied
state.battery: 86 %
ai.featureSet: comfort, ventilation, occupancy, indoor-air-quality

AIoT use cases

Where modeled device data becomes AI value

Predictive maintenance

Vibration, current, temperature, and runtime signals can feed models that detect drift before equipment failure.

Smart buildings

Occupancy, CO2, humidity, light, and energy data can optimize ventilation, comfort, and operating cost.

Utilities and metering

Water, gas, electricity, tank, pressure, and leak data can support anomaly detection and faster field response.

Asset tracking

Location, movement, shock, and environmental signals can help AI workflows identify exceptions in logistics and field operations.

Agriculture and environment

Soil, weather, level, and air-quality data can support forecasts, threshold alerts, and operational recommendations.

Software integrations

Modeled telemetry can be embedded into existing industry software so users act inside the tools they already trust.

Start AIoT

Start with supported devices and a reusable model

Create a free account to evaluate the device catalog, or contact Pilot Things to map your fleet and AIoT workflow requirements.