AI-IoT Topic Guides for Product and Engineering Decisions
Use these guides to choose a practical implementation path across IoT platforms, edge gateways, industrial protocols, AI automation, private knowledge bases, and AI vision.
Read by implementation problem
Each page links to related services, products, and AI technology pages so the guide can lead into action.
AI-IoT Platform
Device onboarding, telemetry, remote control, alerts, and lifecycle management form the foundation for AI-enabled connected products.
Read guideEdge Gateway
Edge gateways handle protocol conversion, local buffering, offline operation, edge AI, and cloud coordination.
Read guideIndustrial Protocols
Modbus, OPC UA, MQTT, serial devices, HMI software, and protocol adapters determine whether field equipment can become useful data.
Read guideDify and Private AI
Dify, LLM workflows, private knowledge bases, and local model deployment need clear app boundaries, data governance, deployment choices, and operating rules.
Read guideVision and Voice AI
Vision and voice AI projects succeed when capture conditions, samples, labeling, model choice, edge deployment, and business workflow integration are designed together.
Read guideFUXA / Node-RED / SCADA
Low-code SCADA and automation tools are useful for fast prototypes, but production projects still need point tables, permissions, logs, deployment, and monitoring.
Read guideESP32 / ESPHome / TinyML
ESP32 projects move from prototype to product only when firmware layers, provisioning, OTA, certificates, logs, power, and platform access are planned.
Read guideAG-UI / MCP / AI Agent
AI agents need clear boundaries, tool protocols, user-visible state, permission checks, and rollback paths before they can safely act inside business systems.
Read guideSmart Home Integration
Smart home and light-commercial products need ecosystem decisions across Zigbee, Matter, Thread, Wi-Fi, Tuya, local gateways, apps, and support operations.
Read guideSmart Manufacturing
Smart manufacturing projects should start from measurable goals such as downtime reduction, yield improvement, energy savings, traceability, quality, or remote service.
Read guideStart from the constraint that blocks the project
If the problem is field connectivity, start with gateway and protocol topics. If the problem is internal productivity or customer workflow, start with AI automation and private knowledge-base topics.
Device and data foundation
Platform, gateway, protocol, telemetry, and remote operations decisions.
AI workflow foundation
AI apps, agents, RAG, automation, vision, and deployment decisions.
Need help choosing the right path?
Send the equipment type, workflow pain point, target systems, and current data status. We will suggest a practical first implementation direction.
- AI + IoT product architecture review
- Hardware, firmware, cloud, and application integration
- Prototype planning and production support