AI Technologies for Product, Workflow, Edge, Vision, and Voice
Choose a technology path based on the workflow you need to change: AI apps, agents, RAG, automation, local AI, industrial vision, or speech intelligence.
Popular AI development technologies for production projects
Each page explains the technology, architecture, services, use cases, delivery process, evaluation method, and related implementation paths.
OpenAI
LLM APIs, multimodal applications, tool-calling agents
View pageLangGraph
Agent orchestration, state machines, multi-step workflows
View pageLlamaIndex
RAG, knowledge bases, document indexing
View pageDify
AI application orchestration and management platform
View pagen8n
AI automation and business system integration
View pageOllama
Local AI, private deployment, local model runtime
View pageYOLO
AI vision, object detection, industrial inspection
View pageFunASR
AI voice, speech recognition, speech-to-text
View pageMatch the technology to a real implementation topic
Dify 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 guideNeed help selecting the right AI stack?
Share your workflow, data source, deployment constraints, and target users. We will help choose a practical first AI implementation path.
- AI + IoT product architecture review
- Hardware, firmware, cloud, and application integration
- Prototype planning and production support