How I Built 4 Production AI Products in 6 Months

说明与用途

作者在六个月内使用共享的 Next.js/NestJS 架构、pgvector 进行 RAG、LangGraph 进行代理工作流，交付了四个生产级 AI 产品：虚拟接待员、PDF 搜索、AI 支持代理和自由职业者招聘服务器。

问题

Clinics, salons, and service businesses miss calls during busy hours; support teams answer repetitive questions; teams waste time searching PDFs; clients find it hard to vet freelancers.

结果

All four products are live, testable, and solve real problems, demonstrating a repeatable production‑first AI development workflow.

工作流

• 1. Define product scope and data sources
• 2. Build Next.js frontend with tRPC/REST
• 3. Implement NestJS backend modules with DI and guards
• 4. Integrate pgvector for RAG and LangGraph for agent workflows
• 5. Add WebSocket for real‑time interactions
• 6. Deploy to production domain
• 7. Conduct weekly live demos and iterate

证据

Clinics, salons, and service businesses miss calls during busy hours. Those callers book elsewhere.

来源

Drop any PDF (even 40+ pages), ask a question, get a cited answer with page numbers.

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Reads your documentation once, then resolves tickets, checks order status, and escalates with full context.

来源

All 4 products share a production‑grade foundation: Next.js Frontend, NestJS Backend, pgvector, LangGraph, WebSocket.

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