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A smart AI restaurant chatbot built with Botpress that provides restaurant information, answers menu questions, and recommends dishes based on customer preferences. It can book, modify, and cancel reservations, check real-time availability, suggest alternative times when a slot is unavailable, and automatically send booking confirmations. An Airtable CRM dashboard is integrated to manage reservations, control table availability, and review customer conversations.
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I built a workflow that turns a business website into a testable AI phone receptionist.
The system analyzes the website, extracts services, staff, opening hours, policies, and other relevant information, then creates an isolated voice-agent demo for that business. Instead of sending a generic recording, we can provide a real phone number that allows the business to test the agent through an actual call.
When the website or scheduling system supports an integration, the agent can check availability and book appointments during the call. Otherwise, it safely captures the caller’s details and preferred time for staff follow-up.
If a business decides to continue after the demo, the same foundation can be expanded into a production receptionist with databases, customer-specific business rules, calendar and CRM integrations, confirmations, call summaries, analytics, and administrative controls.
The extracted information can also reveal gaps in the business website. Its content and structure can then be improved so both visitors and the AI receptionist receive clearer, more reliable information.
The main lesson I share is that website extraction alone is not enough. Information must be normalized, validated, and restricted to the source material before it can be used safely in a live voice conversation.
The demonstration uses a fictional dental clinic and real recordings from my working voice-agent system. No patient data, real customer information, or invented testimonials are included.
I used Descript to assemble and refine the presentation, edit the narration and real-call sections, create captions, and prepare the final submission.
Your AI agent just did 9 things: pulled 412 orders, matched them to Stripe, caught a duplicate £248 charge, drafted a ledger entry, opened a ticket. Whole run cost $0.011.
Now the real design question — what does the user actually SEE?
A — The transcript. A paragraph of text and a collapsed "Used 3 tools".
B — The run canvas. Nine colour-coded rows: what ran, what it cost, what got flagged, and one step that stops and waits for you before money moves.
Same agent. Same run. Two completely different products.
Vote A or B — and tell me why. I build the B version for a living.
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🚀 Built a WhatsApp Ordering & Customer Support Bot for E-commerce!
Instead of customers waiting for replies or manually processing every order, this automation handles the conversation from start to finish. 🤖
Here’s what the workflow does:
💬 Customer sends a message on WhatsApp
🧠 AI understands the customer’s request
📦 Helps with product/order-related queries
📝 Logs the conversation automatically
✅ Detects when an order is complete
📊 Saves the new order to Google Sheets
🔔 Notifies the business owner instantly
💬 Sends a response back to the customer
The goal is simple: faster customer support + smoother ordering + less manual work.
Built with n8n + AI + WhatsApp + Google Sheets.
This is just one example of how AI automation can help e-commerce businesses save time and handle more customers without increasing manual workload.
What would you automate first in an e-commerce business? 👇
#AIAutomation #n8n #WhatsAppAutomation #Ecommerce #CustomerSupport #Automation #AI #WorkflowAutomation #EcommerceAutomation
