AI development stack

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.

Browse technologies Discuss AI development

Engineering team selecting an AI delivery path around business requirements — Start from the workflow and operating boundary, then select the model and framework.

Choose an implementation path

Match AI technology to the job it must perform

Use these six routes to narrow the first release. Each route connects to a detailed technology guide, delivery scope, evaluation method, and related service.

AI apps and copilotsProduct assistants, knowledge search, customer support tools, internal copilots, and AI features embedded into existing systems.Agent workflow and automationMulti-step workflows, API tools, approvals, retries, event triggers, and human-in-the-loop control for real operations.RAG and knowledge basesDocument ingestion, metadata, retrieval, citations, permissions, update workflows, and private knowledge applications.Private and local AIPrivate cloud, local models, Ollama runtime, model gateway, logs, cost controls, and controlled deployment boundaries.Vision intelligenceYOLO detection, OCR, inspection, image evidence, edge inference, false-alarm handling, and system integration.Voice workflowsSpeech recognition, call transcription, voice records, summaries, searchable archives, and voice-enabled product workflows.

Top 8 technologies

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

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LangGraph

Agent orchestration, state machines, multi-step workflows

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LlamaIndex

RAG, knowledge bases, document indexing

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Dify

AI application orchestration and management platform

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n8n

AI automation and business system integration

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Ollama

Local AI, private deployment, local model runtime

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YOLO

AI vision, object detection, industrial inspection

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FunASR

AI voice, speech recognition, speech-to-text

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Implementation scenarios

AI should become an application path, not a disconnected model demo

AI application use cases for enterprise workflows

Enterprise AI workflow apps

Turn knowledge, approvals, forms, APIs, and logs into a managed AI application instead of a standalone chatbot.

Industrial AI vision inspection workflow

AI vision and inspection

Use camera data, detection models, edge devices, and review workflows to support industrial or warehouse quality checks.

AI application development team working on enterprise software

Custom AI software delivery

Connect AI models with product interfaces, device events, permission rules, logs, and the business systems that own the workflow.

Speech recognition and voice analytics workstation

Voice records and operations

Convert speech into searchable records, summaries, quality review signals, tickets, and product-support knowledge.

Related guides

Match 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.

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Vision 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.

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FUXA / 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 guide

FAQ

Questions before starting an AI application project

Clarify the workflow, source data, model boundary, integrations, evaluation, and deployment path before implementation.

How should we choose among OpenAI, LangGraph, LlamaIndex, Dify, n8n, Ollama, YOLO, and FunASR?

Start from the workflow and operating constraints. OpenAI supports hosted model capabilities, LangGraph and LlamaIndex support orchestration and retrieval, Dify and n8n accelerate workflow delivery, Ollama supports local model operation, while YOLO and FunASR address vision and speech workloads. The right stack depends on data sensitivity, latency, integration, evaluation, and maintenance requirements.

Can multiple AI technologies be combined in one product?

Yes. A production system may combine a model API, retrieval layer, workflow engine, local inference, vision, or speech components. The architecture should keep each component replaceable and define clear interfaces, observability, fallback behavior, and acceptance criteria.

Can an AI application be deployed privately or at the edge?

Yes, when the selected models and infrastructure fit the available compute, security, and latency constraints. We assess data boundaries, model size, hardware capacity, update strategy, and cloud dependencies before recommending private, edge, cloud, or hybrid deployment.

What should we prepare before starting an AI development pilot?

Prepare one bounded workflow, representative input data, the expected output, current system interfaces, deployment constraints, and measurable acceptance criteria. A narrow pilot is usually the fastest way to validate feasibility and operating cost before expanding scope.

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