I’m currently working on an AI receptionist for HVAC companies (handling inbound calls + booking jobs). One issue I’m thinking about: 👉 How are you handling real-time interruptions during tool execution? Example: - Caller is speaking - AI is collecting details (name, issue, time) - Suddenly caller changes request or adds emergency info - Or interrupts mid-flow Right now I’m seeing cases where: - The conversation flow breaks - Or the tool gets triggered too early before full context is captured Curious how others are solving this: - Do you rely more on strict state management? - Or dynamic LLM re-evaluation after each turn? - Or interrupt handling at the voice layer (not model)? Would love to hear how you’re structuring this in production systems.

Building AI voice receptionist for HVAC (2-10 tech shops) — want builder input Building a 24/7 voice receptionist for small HVAC shops (owner-operator, 2-10 techs). Job: answer every call, book directly to calendar, route emergencies correctly, stay in lane on pricing/diagnosis. Currently on Retell AI + Cal.com, no CRM layer. Four things I want real experience on, not theory: Feature list beyond booking — what's non-negotiable that I'm not thinking of? Reminder calls/texts before appointment? No-show follow-up? Anything you built that turned out to be the actual differentiator once live with a real client? Follow-ups — is anyone running reminder/no-show sequences natively off Retell + Cal.com, or does that always require bolting on something else? At what point did you need it — client 1, or later? Single Prompt vs Conversational Flow — for straightforward booking flows (not complex support), has anyone actually run both and compared? Where does Single Prompt start breaking down in practice, and at what call complexity? CRM — running lean with just the calendar right now. What actually breaks without a CRM layer, and at what client count did you need one? What's the minimum viable setup people add first? Real numbers/experience over theory appreciated.

Everything seems pretty easy when you're getting the first version together, but where does it start getting complicated or breaking down when you're trying to actually finish and launch it? I just want to know what to expect!

After several rounds of testing, I’m happy to say the Real Estate AI Voice Agent I’ve been building inside GoHighLevel is ready to deploy. ✅ Over the last few days, I’ve: - 🧠 Refined the master prompt - ⚙️ Added and improved system prompts - 📅 Integrated appointment booking through the GHL calendar - 🔄 Added rescheduling - ❌ Added cancellation - 🔍 Added availability checking - 📚 Connected the Knowledge Base - 🧪 Tested the agent multiple times with different scenarios, questions, and tasks I specifically wanted to make sure it could handle more than just the “happy path” and behave properly across different types of conversations. After multiple tests, everything is working as expected. The agent is now ready for deployment. 🚀 Now I’m looking at the next step: what should I build next? Would love to hear ideas from the community — especially features that could solve a real problem for real estate businesses. What would you add? 👇

Finally fixed my demo — need your honest feedback 👇 Quick update: I reworked my AI receptionist demo based on your feedback. Old version: → 2:49 long → Trying to show everything → Honestly… felt like a tutorial, not a demo New approach (much simpler): 1. Call first (no distractions) 2. Show the moment it turns into a booked job 3. Then quick “missed calls → revenue” proof That’s it. Also added a basic dashboard to track: • Missed calls • Booked jobs • Revenue recovered vs lost One week test data: → $4,150 recovered → $18,200 lost from missed calls → 50 missed calls Made me realize… This isn’t a “cool AI demo” It’s literally a revenue leak problem Where I need your help: I’ve cut the demo down, but want to make sure it actually converts, not just looks good. For those who’ve landed clients: 👉 What’s the ONE moment in the demo that made people say “I need this”? Is it: • The voice realism? • The booking moment? • Or the revenue proof after? I’ll drop the updated version in the comments shortly. Would really appreciate honest feedback 🙏 #AIVoiceAgent #HVAC #LeadConversion #SalesSystems #BuildInPublic #ClientAcquisition