Been building a full AI workforce system on Retell + n8n + GHL for home service clients. Got multiple agencies asking about white-labeling it, so putting this out there formally. What the system does: • AI Voice Rep: Retell agent handles inbound calls 24/7, qualifies leads, books appointments directly into GHL calendar • AI Text Rep: n8n orchestrates all SMS/WhatsApp/Chat/Facebook/Instagram DM conversations using OpenAI • Outbound campaigns: AI calls cold lead lists, n8n triggers Retell, outcomes logged back to GHL • Review requests, reactivation sequences, pipeline updates — all automated inside GHL The tech stack: → Retell AI (voice agent, inbound + outbound) → n8n (orchestration, webhook triggers, conversation routing) → Supabase (conversation memory, contact state tracking) → OpenAI / OpenRouter (conversation AI, prompting layers) → GHL (CRM, pipeline, calendar, workflows, tags) → Twilio (A2P SMS, voice fallback) Deployment channels: SMS, WhatsApp, Live Chat, Facebook Messenger, Instagram DMs, Inbound Voice, Outbound Voice. For agencies interested in white-labeling: Model options: 1. Setup fee ($1,497) + you manage clients, you keep the MRR 2. Revenue share — I handle setup + maintenance, we split the monthly retainer 60/40 Clients are home service businesses: HVAC, plumbing, roofing, cleaning, electrical. Average client value: $997–$1,997/month. HVAC client result from Week 1: 23 calls handled by AI, 9 bookings, $3,800 recovered from missed calls. If you’re already working with home service clients on GHL and want to add this as a productized service — DM me. I can walk you through the system and we can figure out if the white-label makes sense for your setup.

For the first 6 months I built every voice AI system custom. New Retell agent. New n8n workflow. New GHL sub-account configuration. Every time. It worked. But it didn’t scale. Then I started using a pre-built AI workforce snapshot as the base layer. Here’s the exact tech stack inside the snapshot: → Retell AI — inbound voice agent, sub-500ms latency, live call routing → n8n — central orchestration layer (all webhook logic lives here) → GHL — CRM state, workflow triggers, SMS delivery → OpenAI — conversation intelligence and qualification logic Instead of rebuilding, I import the snapshot, swap in the client’s Retell API key and phone number, and the full system is live in 48 hours. What the system covers: → Inbound voice agent (after-hours, qualification, booking) → Missed call SMS text-back (60-second response) → Database reactivation (outbound SMS to dormant leads) → Review automation (24h post-job) Margin model: Snapshot: $97–$297/mo Client billing: $497–$997/mo DB Reactivation is the fastest ROI module. First client result: $3,800 recovered in Week 1 from a 14-month-old lead list. The n8n + Retell + GHL stack is the right combination for this. n8n handles all the routing logic, GHL executes the CRM actions, Retell handles the voice conversation. Who else is using this stack? What’s your snapshot deployment workflow?

Hey everyone 👋 I’m Saad, an AI/ML Engineer and developer, and today I’m excited to introduce something I’ve been building: OQVERA. I didn’t want to create another website that simply says: “We build AI solutions.” I wanted to build something that shows what I can actually build. So OQVERA is becoming my public engineering portfolio — a place where I showcase real projects, AI systems, automation workflows, agentic applications, and the technical work behind them. What you’ll find inside OQVERA 👇 🤖 AI & Agentic Systems Multi-agent architectures, AI agents, RAG systems, LangGraph workflows, intelligent assistants, and LLM-powered applications. ⚙️ Automation & Workflows Business process automation, API integrations, AI-powered workflows, calling agents, and systems designed to remove repetitive manual work. 💻 AI Engineering & Software Backend systems, APIs, AI integrations, computer vision, intelligent applications, and production-focused engineering. 🔬 Case Studies I’m documenting projects not just by showing the final UI, but by explaining the problem → architecture → technology → implementation → outcome. Why I built OQVERA I want my work to speak for itself. Instead of telling potential clients: “I can build an AI agent.” I can show them one. Instead of saying: “I know automation.” I can show the workflow. Instead of listing 20 technologies on a CV... I can show what I built with them. That’s the direction I want to take with OQVERA. And yes — I’m building this with clients in mind. If you're a founder, business owner, startup, or team looking at ways to use: - AI agents - LLMs - workflow automation - intelligent customer support - AI calling systems - RAG / knowledge systems - custom AI software - internal business automation I'd love for you to check out what I'm building. 🌐 OQVERA: https://oqvera.netlify.app/ This is only the beginning. I’ll be continuously adding new projects, experiments, architectures, and case studies as I build.

Can anyone help with my appointment automation - Member id for subscription clients - In store mobile appointment services - Via text examples pictures of my haircuts - Looping reminders automation

Hi, I’m Sabiha Sabi Shammi. I work with Google Ads and conversion tracking I’m interested in learning AI automation Could you please guide me on where and how I should start learning AI automation so I can learn it correctly and in an organized way? Thanks in advance