Custom IoT Architecture Integration and Predictive AI
Custom ecosystem connecting edge hardware (RFID, LoRa, BLE) with corporate ERPs and Machine Learning models. Architecture designed for the client's specific operation.
The Challenge
Custom ecosystem connecting edge hardware (RFID, LoRa, BLE) with corporate ERPs and Machine Learning models. Architecture designed for the client's specific operation.
Context
Client with a particular operation that the standard RFID catalog does not fully cover: combination of asset mobility, variable read range, and the need for predictive models over captured events.
Approach
Diagnostic defined the edge architecture and the points where each technology (RFID, LoRa, BLE) delivers differential value. Bounded Deployment validated three-channel integration. Full Project extended coverage and activated Machine Learning models over the event stream.
Architecture
Mixed edge hardware: passive RFID, active RFID, BLE beacons, LoRa sensors · native API integration with corporate ERP · ML models over historical events · private instance for sensitive data.
Resultados ampliados
- Model-assisted operational decisions over real-time events.
- Analytical capability deployable over new processes without redesigning the capture architecture.
Implications
When the operation is particular, the catalog is a starting point. Custom architecture pays its difference in evolution speed.
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