As I mentioned in the last video, I will be posting a full breakdown of the AI receptionist, with the majority of votes for the AI receptionist. Here we go! With the blueprints. Feel free to let me know if you have any questions.

Have you ever wished your WhatsApp could reply to clients instantly, even when they send voice notes or images? That was exactly what my client had in mind. He wanted a system that could automatically understand different message types, process them, and reply intelligently without needing constant human effort. The problem was simple, too many customer messages coming in through WhatsApp, from text to audio to images. Sorting, understanding, and replying manually took too much time and slowed down his workflow. His goal was to automate conversations while keeping them natural and accurate. I built the entire automation using n8n, integrating WhatsApp Cloud API, OpenAI models, and Google Sheets. The workflow starts with a WhatsApp trigger, checks if the message is text, audio, or image, then processes each accordingly, downloading, transcribing, or analyzing before merging everything into one flow. From there, an intent classifier routes the message to the right OpenAI model to generate the perfect response. The biggest challenge was synchronizing multiple data types without breaking the logic. I solved it by implementing a flexible switch system, prompt memory, and precise intent routing. The final result was seamless, a WhatsApp bot that runs 24/7, handling every message type with human-like understanding. The client was genuinely impressed; what once took hours now happens instantly. This kind of automation is perfect for agencies, service businesses, coaches, and eCommerce brands that deal with high message volumes daily. If you’ve ever wanted to simplify how you handle conversations or scale your communication without losing the human touch, let’s talk, I’d be happy to guide you on how to make it work for your business.