This is an overlooked goldmine where local businesses actively show up looking for help, and almost no AI experts are there. Ming Xu, CIO of Trillet, explains how he positions himself as the only AI service in the room and turns simple conversations into paying clients. If you’re trying to land clients without competing online, this changes the game.

I have previously conducted lectures on LLM orchestration, RAG pipeline, multi-modal models, and multi-agent architecture. I am going to explain how to implement chatbot functionality by utilizing the previous lecture. A chatbot MVP is essentially: A system that takes a user message → understands it → optionally looks things up → generates a response → returns it You can express this as a simple loop: The 5 Core Components of a Chatbot MVP Break the system into 5 understandable parts: ① User Interface (UI) Chat screen (web, app, Slack, etc.) Where users type messages ② Backend Controller (Orchestrator) The “brain” that decides what to do next Routes requests between components Connect to your previous lectures: This is where **LLM orchestration logic** lives. ③ Large Language Model (LLM) Generates responses Understands natural language ④ Knowledge / Data Layer (Optional but critical for MVP+) Documents, database, APIs Used in **RAG (Retrieval-Augmented Generation)** ⑤ Memory (Optional but powerful) Conversation history User preferences User ↓ UI ↓ Orchestrator ├── LLM └── Knowledge Base (RAG) ↓ Response contact information: telegram:@kingsudo7 whatsapp:+81 80-2650-2313

The replay from last week's live call with Ming Xu (CIO of Trillet.ai) is now in the classroom in AWA PRO. He shared what’s actually working right now to land voice AI clients and grow an agency in 2026. View in AWA PRO Classroom 👉 https://www.skool.com/automate-what-academy-pro/about