Real estate

Post LinkedIn lead magnet · Real estate

We tried remote AI Engine kickoffs. They stalled. I've shipped five engines in 60 days. Sales, marketing, operations, finance, recruitment. Different verticals, different stacks, different team sizes. The one constant: the build only really starts when I'm in the room with the client. Day 1 on-site in Cypress, TX. By 5 PM we had 5 of 7 skills built and 4 real leads sitting in HubSpot. Ben (the GM) and Mark (sales) watched it run in real time. The trust we built that day carried the next four weeks of remote work without friction. Same shape on a Real Estate Broker earlier this month. Three hours in the room. The team brought stuff to the table they would never have written in a Loom or typed in a Slack thread. Edge cases, internal politics, a quoting workflow nobody had documented. All of it surfaced because we were sitting next to each other. If you're a fellow agency owner watching this and quoting fully-remote builds, I get it, the math looks better on paper. The math also assumes the build doesn't stall. Ours stalled when we tried it remote-first. The fix was a flight. Cost of an on-site kickoff: about $1,500 in travel. Time saved on a Pro AI Engine build: 1–2 weeks. Trust earned: the difference between invoice 2 paid on time and the build dragging into a fifth month. Comment "BUILD" and I'll send you the playbook we use to ship AI Engines. Architecture, intake sheet, scoring rubric, module cadence, and how the partner referral channel works. #AIEngine #AIAgency #BuildInPublic #AIAutomation #SDVOSB #AIOS #AIOperatingSystem

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Comment "BUILD" and I'll send you the playbook we use to ship AI Engines.

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Real estate

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J'ai créé le chatbot le plus puissant qu'on puisse avoir en France : Claude X Data Gouv... Je t'explique tout Le concept est simple. Je pose une question. Claude analyse. Il me répond avec les vraies données officielles françaises. Comment ? J'ai couplé Claude à DataGouv via le MCP officiel. DataGouv c'est LA plus grande base de données publiques et fiables de France. Immobilier. Santé. Entreprises. Budgets. Transport. Éducation... Tout est là. Tout est officiel. Tout est vérifié. ( Et c'est open source ) Le problème ? Ces données étaient inaccessibles pour 99% des gens. Fallait savoir coder. Télécharger des CSV. Comprendre des API. Grâce au MCP, Claude se connecte directement à cette base. Il cherche. Il sélectionne le bon dataset. Il analyse. Il répond. En langage naturel. En quelques secondes. « Quels sont les prix immobiliers à Lyon par arrondissement ? » “Quelles communes de plus de 20 000 habitants ont une forte croissance démographique mais peu de cabinets de kinésithérapie ?” “Où y a-t-il une forte densité de jeunes actifs 25–35 ans mais peu de formations tech ?” “Quelles régions distribuent le plus de subventions à la digitalisation ?” Réponse instantanée. Source officielle. Zéro hallucination. C’est ça qui change tout. Pas une IA qui invente. Une IA qui sait où chercher la vérité. J'ai créer un ChatBot de ZinZin qui me permet de faire des analyses concrètes, de faire des études concurrentiels, étudier les opportunités, ou et surtout dans quel secteur d'activité je peux implémenter l'IA par exemple... C'est juste énorme Tu me connais, je pense, je t'ai fais : - Un Guide complet qui explique comment utiliser et créer une tel app - Les prompts au complet - L'accèes à ce fameux MCP ( Le repo GitHub ) - Des exemples d'utilisation Commentez " DataGouv" et je te l'envoie Je vous invite et republier ça peut clairement aider votre réseau

Commentez " DataGouv" et je te l'envoie

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Real estate

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I mapped every AI automation opportunity across 25 industries. 10-15 pain points each. With the exact positioning, pricing range, and who to sell to. This took me 4 years and 80+ client engagements to figure out. A lot of AI Agencies pick a niche and pray. They don't know the actual pain points. They don't know who the buyer is. They don't know what these companies are already paying for broken solutions. They don't know what the realistic project size is. So they end up competing on price for generic "AI automation" gigs. I've worked with marketing agencies, recruiting firms, e-commerce brands, law firms, real estate companies, healthcare practices, financial services, SaaS companies, manufacturing, construction, logistics, and more. Every single one has 10-15 processes that are bleeding money because they're still done manually. Here's what the guide covers for each industry: → The top 10-15 automation pain points (ranked by ROI) → Who the actual buyer is (CEO, COO, ops manager, etc.) → What they're currently paying for manual labor or broken SaaS → Realistic project pricing ($5K-$60K+ depending on scope) → The discovery questions that unlock the deal → How to position yourself as the expert even if you've never worked in that industry → Red flags to avoid (industries and company sizes that aren't worth it) 25 industries and 300+ specific automation opportunities. This is the cheat code for picking your niche and knowing exactly what to sell before you ever get on a call. What the full guide? 1. Like this post 2. Comment "NICHE" and I'll send the guide (must be connected) Bonus for the first 100 people: I'm also including an ROI calculator you can use on prospect calls. Plug in their numbers and show them exactly what they're losing to manual work every year.

Comment "NICHE" and I'll send the guide

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