I built this system for a locksmith company that needed a better way to handle incoming emergency calls and assign jobs to technicians automatically. Here’s what the system does: • AI answers incoming calls • Collects customer details (name, address, issue, phone number) • Creates a job record in the database • Sends the job to technicians one by one • Includes Accept / Reject buttons inside the email • Waits for a response • If ignored → automatically escalates to the next technician • Stops immediately once someone accepts The main challenge was building the loop logic in n8n so it doesn’t notify everyone at once, and making sure the workflow instantly stops once the job status changes to “accepted.” Result: Replaces manual dispatching, prevents double assignments, reduces missed calls, and ensures technicians are assigned faster without a human coordinating everything. I recorded a Loom walkthrough showing: – A live AI call test – The n8n workflow logic – How accept/reject works – How the escalation system is structured If anyone’s curious about the backend setup, here’s the full breakdown: https://youtu.be/fG83tKiWcgA Would love to hear how you’d improve the routing or escalation logic.

Hey everyone, I’m building a live AI phone receptionist and I’m facing an issue when the assistant has to repeat numbers back to the caller. Problems: • When a caller gives an 11-digit phone number → digits merge or sound unclear • When repeating prices like £1500 → pronunciation sounds distorted • Works fine sometimes, but inconsistent on real phone calls Stack: Vapi + Twilio + n8n + ElevenLabs (also tested Gemini/OpenAI) Tried already : – Increasing end-of-turn timeout (0.5 → 2s) – Changing voices/models/LLMs How do you normally solve this in production systems? Is it formatting, TTS settings, buffering, or another approach?

A lot of people talk about “doing outbound consistently,” but what usually breaks isn’t effort it’s friction. Recently, I built a LinkedIn outreach system to remove that friction completely. What the system does: - Scrapes targeted LinkedIn leads - Researches each profile - Writes a personalized connection message per lead - Sends the connection request - Automatically sends the 1st and 2nd follow-up What this removes from the process: - Searching and opening profiles - Copy-pasting profile data - Writing messages one by one - Manually remembering and sending follow-ups Realistic time impact:~20 minutes saved per lead. At ~30 leads/week → ~10 hours saved every week. The bigger outcome: Outbound runs in the background. Messages stay personalized.Follow-ups never fall through. If you’re curious and want to see the exact workflow or explore setting something similar up for yourself, feel free to DM me and we can walk through it on a quick call.

Just wrapped up a new automation build for a dental clinic, so sharing it here. What I built: A complete AI voice agent using Vapi + n8n that handles all patient interaction without needing staff involvement. What it actually does: Answers calls 24/7 Handles basic FAQs Books, cancels, and reschedules appointments Pushes call summaries + appointment info into Google Sheets Works during after-hours and peak hours Fully customizable per clinic workflow I've attached the demo video Why it matters: It helps the clinic miss fewer leads, convert more patients, and ultimately increase monthly revenue without needing extra staff. If anyone here runs a service business or wants a similar voice agent setup (or a chat/WhatsApp agent), I can build one tailored to your workflow. I keep pricing affordable so you don’t have to worry about budget while getting a high-quality, fully functional automation system.

Hey everyone, I wanted to share a win that might help anyone here struggling with missed calls or manually qualifying leads. I just finished deploying a custom AI Voice Agent that handles inbound and outbound calls to qualify leads and book appointments directly into the calendar. This system runs on a highly efficient n8n/Vapi flow that I built, which connects to the client's CRM. The flow does three key things: 1. Real-Time Booking: Automatically checks Google Calendar availability and handles full booking logic. 2. Zero-Latency Voice: Uses Vapi/Retell to ensure the agent sounds natural and responds instantly. 3. Advanced Integration: Saves client information (name, qualified lead status) immediately to the CRM via dedicated API. It’s been fascinating to build, and it completely frees up the client's sales team. Questions for the Community: - What's the biggest bottleneck in your current lead qualification process? - Has anyone else here successfully deployed a Vapi AI solution? What was your stack? Happy to answer any technical questions about the Bot