How I Built 4 Production AI Products in 6 Months

Описание и применение

В течение шести месяцев автор создал четыре продакшн‑продукта с ИИ, используя общую архитектуру на Next.js и NestJS, pgvector для RAG и LangGraph для агентских потоков. Продукты включают виртуального ресепшн‑бота, поиск по PDF, AI‑агента поддержки и сервер для найма фрилансеров.

Задача

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.

Результат

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

Рабочий процесс

• 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

Доказательства

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

Источник

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

Источник

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

Источник

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

Источник