I’m working on an automation system for real estate offices, and I’d love honest feedback from anyone who has worked in this field. It’s built for the inside of the office, a shared brain for the team: - A clean, organized database for all units, with different permissions for different people, so everyone can work and communicate more easily - Lead matching: when a client asks for a unit the office doesn’t have, the lead is saved. Every time a new unit enters the system, it’s checked against those saved leads, so employees don’t forget them - A lead cycle: when a unit is rented, the contract details go into the system. As the contract nears its end, the system reminds the employee to call the tenant. If the tenant isn’t renewing, that’s a new lead. The owner is also contacted, which means new data and a new unit for the office. Then it starts again I’m still learning this field, so I’d really like to hear from people who have worked in real estate: - What problems do you face day to day that I may be missing? - Does this cycle match how it actually works, or is something off? Any honest criticism is welcome. Thanks!

Hey everyone, I'm looking to get some help with Retell AI integrations for a client of mine. Specifically, Retell AI with 3CX, n8n and Airtable. Comment below if this is your area of expertise

Real estate agents run ads on Facebook and Instagram, leads come in, and then nothing happens for hours. The agent is at a showing, driving, or asleep. By the time they reply, the lead has already talked to someone else. I’ve been building a follow-up flow for a real estate client to fix exactly that. A new lead gets a reply within minutes, gets asked one simple question about what they’re looking for, and is nudged toward booking a viewing. The agent only steps in once the lead is warm. The ads weren’t the problem. The gap between “lead comes in” and “someone responds” was. If you work with agents or any lead-based business, how fast is the first reply to a new lead right now? Minutes, hours, or the next day?

Last month I found a hole in the intake prompt of the phone agent I demo to plumbing and HVAC shops. It collected name, address, callback number and the problem, then sent the call details to an n8n workflow. Fixed keywords like "gas smell" or "sewage backing up" flagged the call as urgent so the owner got a text first. Then I went through an open-source intake template for contractors and saw they handle danger before anything else. If the caller mentions a gas smell or a CO alarm, or anything electrical that's sparking or wet, the agent stops and tells them to get out and call 911. Before it asks for a name. Mine would have asked someone smelling gas to spell their street name. I added it as step 0 and checked the live prompt through the Retell API afterwards. If you build intake flows, voice or chat, what do you handle before collecting any details?

Version 2 broke in ways I didn’t expect, so here is what I changed and what it taught me. Although it's a second version, I have tried implementing in 3 more other ways and this is the final one I chose that is more optimized!! Hi all, Hope everyone is doing well!! What it does - Chats with property buyers on WhatsApp Qualifies them (budget, location, timeline, financing) Saves them to Supabase Matches properties Books site visits on Google Calendar Asks a human on Slack when it isn’t sure instead of guessing. Based on the suggestions given my the community memebers, I have made changes in my project and thanks for helping me out by suggesting/questioning about the project. I have learned a lot while making these changes in my project..😇 The 7 requirements I set at the start, and where I am: 1. Save lead (insert, update, lookup): ✅ One upsert per WhatsApp number, no duplicates, saved record read on every message. 2. Warm/cold appointments + partial matches: ✅ Any lead can book if they ask. Partial matches get alternatives based on their top priority (priority_match). 3. Uncertain property questions go to a human: ✅ Pets? Home theatre? Hold this property? Price negotiation? Asks the team, doesn’t guess. 4. Persistent approval workflow: ✅ A separate workflow takes the team’s decision from Slack, stores it, and the agent replies in its own words. (24h WhatsApp window handling still to add.) 5. No match / vague requirements escalate: 🟡 Built, edge cases still in testing. 6. Booking verified before Slack is told: ✅ Event ID and confirmed time checked first. 7. Busy-calendar test: 🟡 Slot rechecked right before booking. Deliberate clash test is next. The biggest lesson: let the AI handle conversation and judgment, and let code handle anything that must be exactly right (verifying a booking, checking the calendar, building an alert).