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? 👇

I’ve been thinking about something lately. A lot of businesses don't actually have a people problem. They have a repetitive-work problem. Leads sitting in inboxes. Customers waiting for replies. Quotes being prepared manually. Teams copying data between systems. Follow-ups getting forgotten. Reports taking hours. And sometimes, the most expensive problem is the one nobody notices: A customer reaches out after hours, nobody responds, and they go to a competitor. AI and automation can now handle a surprising amount of this. But I’m curious about something from people actually running businesses: What is the ONE process in your business you wish could just run itself? Drop it in the comments. Tell me what happens, how you're handling it today, and roughly how much time it takes. I’ll pick a few and share how I would automate them. Maybe your biggest headache has a surprisingly simple solution. 👀

I thought I'd share one of my automations that I did last week with the community here as part of my learning journey. I have uploaded the YouTube Subscriptions Ingestor for public consumption. After the initial setup the skill will pull your subscription videos from creators and produce .md files to dump in a raw folder to be processed for those running a second brain on their systems to create searchable knowledge bases. Attached below is an infographic detailing the skill's capabilities, a progress in Obsidian as I further refined the skill, along with screenshots of the Markdown files it generates. A key feature is the prompt system—it allows you to choose between a fresh data fetch, grabbing the latest videos, or performing a local search, which saves significantly on token usage. Thanks again to the wonderful skool communities for providing feedback and answering my questions as I move along my AI journey of skill building and AI automations. Peace, Love, and Happiness everyone! https://github.com/SomewhereSimulated/youtube-subscriptions-ingest

A simple business question shouldn’t turn into an hour of manual reporting. Pull the data. Clean it. Check it. Build the chart. Write the summary. Export the PDF. Send it. That’s the workflow I wanted to remove. So I built Plemdo AI Enterprise Analytics. A conversational analytics system where the starting point is simply a business question. The production version I delivered a more advanced version of this system to a client for $3,500. The production implementation went beyond the public demo and included: - Automated data preprocessing before analysis - Claude-based prompt and reasoning skills - Microsoft Fabric / Power BI skills for visual dashboards - Multi-channel interaction across WhatsApp, Telegram and Slack - A real-time Streamlit dashboard - Cloud PDF archival through Google Drive - Subscription and billing workflows The real challenge wasn’t calling an LLM. It was making the data, reasoning, visualization, reporting and delivery layers work together reliably. The base version I’m releasing publicly I wanted developers to be able to study and build on the core idea without publishing the full client implementation. So I created a simpler open-source version: WhatsApp → Conversation Router → Gemini → Data Analysis → Visualizations → Executive PDF → Email + WhatsApp Delivery The public workflow includes: ✅ intent-based conversation routing ✅ context-aware analytics ✅ dynamic chart generation ✅ automated executive PDF creation ✅ email and WhatsApp delivery ✅ full n8n workflow and setup documentation One demo query is: “Show me top beaches in Galway with pubs.” The dataset is just an example. The interesting part is the workflow behind it. Instead of: Question → Analyst → Spreadsheet → Charts → Report → Email you get: Question → Automated Analytics Workflow → Decision-Ready Output That’s the part of AI automation I find most useful — not just getting a model to answer a question, but building the system around it so the answer becomes something the business can actually use.

I’ve been working on a real estate AI voice agent, and I’ve now built the core system entirely inside GoHighLevel. The agent can currently: 📞 Handle inbound calls 📅 Check calendar availability ✅ Book appointments 🔄 Reschedule appointments ❌ Cancel appointments 🗓️ Manage appointments through the GHL calendar 📝 Collect information from callers What I like about this build is that I didn’t rely on a separate n8n workflow or external voice-agent platform. The whole agent is built inside GHL using its AI voice capabilities, functions, and calendar. The goal is to create something a real estate agency could actually deploy to handle incoming calls and appointment management automatically. Now that the core is working, I’m thinking about what to add next. What feature would you add to make this agent more valuable for a real estate business? Would love to hear your ideas 👇