Real AI builds for real estate agents.

What you build

A local agent runtime on a Mac mini in your office. MCP servers expose your iMessage threads, Apple Notes, and Calendar to the agent. It indexes everything and answers natural-language questions over your real client history. No vendor in the middle, no monthly subscription, your data stays on your machine.

The stack

Hermes Agent or OpenClaw + MCP servers (iMessage / Notes / Calendar) + a Mac mini M-series with 16GB unified memory (or your existing laptop).

Repos to study or fork

• modelcontextprotocol/servers

  Anthropic's official MCP server reference repo. Filesystem, fetch, Postgres, SQLite, Brave Search all included. Clone this first to understand how MCP works before wiring iMessage / Notes / Calendar.

• steipete/openclaw

  OpenClaw daemon - the easiest local agent runtime to set up. Search GitHub for the latest; the README plus the example skills folder will get you started.

• mcp-server-imessage

  Community-maintained iMessage MCP servers. Search GitHub for this and pick one updated within the last 30 days. Same approach for mcp-server-apple-notes.

How this adds value

For you

No more "wait, what did they say last month?" The answer is one prompt away. 10-15 minutes saved per client interaction, multiplied across your book.

For your business

Clients feel seen and remembered, which is what they pay you for. Referral rates lift because you're the agent who remembers everything - names, deal breakers, school preferences, the spouse's job change.

For your clients

They get the experience of working with someone who actually paid attention. Their kid's name, the contractor estimate they mentioned, the price ceiling they walked back from. Recalled accurately, every time.

The caveat

Privacy. Your client data lives on your machine, not in the cloud. Encrypt the drive. Don't sync the agent's memory to iCloud. If you ever sell the Mac mini, wipe it like it's full of client contracts - because effectively it is.

What you build

Your CRM or website lead form fires a webhook into a Cloudflare Worker (or any cheap host). The Worker sends you a Telegram message with the lead's details and inline buttons: DRAFT VOICEMAIL / SCHEDULE TOMORROW / NOT NOW. Tap a button and the bot generates exactly what you need - the voicemail script uses your past client data and your voice from prior conversations, so it doesn't read like a chatbot.

The stack

Cloudflare Worker (or any webhook host - Vercel, Railway, a VPS) + python-telegram-bot or aiogram + Claude API + your existing CRM webhook output.

Repos to study or fork

• python-telegram-bot/python-telegram-bot

  The canonical Python Telegram bot library. Inline keyboards (the APPROVE / SKIP button pattern we use in our own content-engine) are documented under InlineKeyboardMarkup. Look at the examples folder first.

• aiogram/aiogram

  Async alternative if you want better performance or prefer async/await syntax. Same functionality, different ergonomics.

• anthropics/anthropic-sdk-python

  Official Anthropic SDK for calling Claude. The README has the simplest possible example - send a prompt, get a response. Build the voicemail-draft prompt around that.

How this adds value

For you

90 seconds per lead intake. First reply in under 5 minutes instead of 'I'll call them back tonight' that turns into next week. Decision fatigue drops because the bot already did the cognitive work.

For your business

Speed-to-lead is the killer conversion metric. The 5-minute response window is cited everywhere because it actually works. Leads that get a human voice within 5 minutes convert at multiples of leads that get 'I'll get back to you.'

For your clients

They get a real, voice-recorded response in minutes - not a chatbot, not silence. Their first impression of you is responsiveness, which sets the entire relationship.

The caveat

Your voice has to be in the prompt. Feed it 5-10 examples of your actual past first-replies to leads. Without them, the bot writes generic and the lead can smell it before they listen to the second sentence.

What you build

A cron-triggered script that reviews the last 7 days of your iMessage, Slack, and email. It uses Claude to identify "promises you made" - callbacks, comp requests, property tours, follow-ups - and sends a morning Telegram each day with the list. "You owed the Smiths a callback by Thursday, the Johnsons a price comp by tomorrow, the Wilsons a property tour proposal..." The bot remembers when you forget.

The stack

Claude Code skill OR a small Python script + iMessage MCP + Slack bot or bolt-python + Gmail IMAP or Gmail MCP + a cron job on your Mac or a VPS + Telegram bot for delivery.

Repos to study or fork

• modelcontextprotocol/servers

  Has filesystem and Gmail integration patterns. The Gmail MCP server in particular is what you wire up for the email side of this. Read the auth patterns carefully - OAuth setup is the longest part.

• slackapi/bolt-python

  Slack's official Python framework. You won't post anything - you're just reading your own DMs and channels for commitments. The Bolt examples cover read-only usage cleanly.

• anthropics/anthropic-cookbook

  The 'extracting structured data' patterns are exactly what you want here. The prompt that distinguishes 'a real commitment' from 'chat noise' lives in this style.

How this adds value

For you

Never drop a promise. The bot remembers when you forget, which is genuinely most of the time once your book gets past 30 active clients.

For your business

Trust compounds when you always do what you said you'd do. The agents who win on referrals are the ones who never let a commitment slip - this is the tool that lets you scale that without scaling your memory.

For your clients

They get the response when you said they'd get it. Not 'next week,' not 'remind me again,' not silence. The single most uncommon trait in real estate is reliability. This is how you ship it.

The caveat

Distinguishing real commitments from chat noise needs prompt-engineering iteration. Expect 2-3 weeks of tuning before false positives drop to acceptable. Start by labeling 50 examples by hand - what counts as a commitment, what doesn't - and feed them into the prompt as few-shot examples.

What you build

Telegram bot that takes an address as input, pulls public listing data (Zillow, Redfin, Realtor.com - whichever doesn't block your scraper), fetches 5 comparable properties, runs your custom analysis prompt over them, and drops back a formatted PDF in 90 seconds. Built once, used forever, no Saleswise subscription.

The stack

Telegram bot (python-telegram-bot) + a scraper (Playwright or crawl4ai for the harder sites) + Claude API for the analysis + ReportLab or pdfkit for PDF generation + a cheap VPS or cloud function.

Repos to study or fork

• unclecode/crawl4ai

  AI-friendly web crawler. Handles JavaScript-rendered pages (Zillow) better than plain BeautifulSoup. The output is already structured for LLM consumption, which saves you a parsing step.

• microsoft/playwright-python

  If crawl4ai isn't enough, drop down to raw Playwright. Slower to write, but bulletproof for sites that aggressively block scrapers.

• reportlab

  The Python PDF generation library most CMA tools end up using. Search GitHub for examples - the docs are dense but the patterns are well-documented.

How this adds value

For you

90 seconds vs 2 hours per CMA. "Do you have comps for this?" no longer eats your afternoon. Listing appointments stop being gated on prep time.

For your business

Faster turnaround wins listing appointments. The seller who's interviewing three agents picks the one who shows up with comps ready, not the one who says "I'll send you something this weekend."

For your clients

Get a real CMA when they ask, not a delay. The trust signal of "I can pull this in 90 seconds" is real - it tells them you're operating at a different level than other agents.

The caveat

Scrapers break when Zillow or Redfin change their HTML. Budget 1-2 hours per month for maintenance. If you outgrow scraping (volume or fragility), graduate to a paid data API - RentCast, HomeJunction, or Realtors Property Resource (if you have access via your MLS).

What you build

A custom Claude Code skill that lives in ~/.claude/skills/listing-prep/ (or a private GitHub repo you load as a plugin). It encapsulates your entire listing-prep workflow: fetch comps, draft an MLS description in your voice using 5-10 of your past best listings as examples, generate the social content set, pre-fill the presentation deck. One command, hours of work done in minutes - in your voice, not a generic template.

The stack

Claude Code (the CLI tool) + a custom skill repo on GitHub + your past listings as voice examples (committed as MDX or plain text in the skill's references/ folder).

Repos to study or fork

• anthropics/anthropic-cookbook

  Has skill patterns and Claude API examples. Read the 'tool use' and 'extended thinking' sections - they're the building blocks of any skill that does more than a single prompt.

• mvanhorn/last30days-skill

  The skill we use to mine signals for our own blog (search GitHub for the latest version). It's a working example of a non-trivial Claude Code skill - read the SKILL.md and the scripts/ folder to see how a real skill is structured.

• your-name/listing-prep

  This is the one you create. Your repo, your voice examples, your team's prompts, versioned in git. Share read access with your team so everyone on your brokerage runs the same workflow.

How this adds value

For you

Hours of repetitive prep collapse into minutes. Cognitive load on routine listings drops to zero, which frees attention for the parts that actually matter - the seller conversation, the strategy call, the staging walkthrough.

For your business

More listing appointments per week because prep is no longer the bottleneck. Your team can run the same skill - which means your operating standard is consistent across every agent in your office.

For your clients

Their listing presentation feels more polished because you put energy into the parts AI can't do (the local-market storytelling, the pricing strategy nuance). The mechanical work was already done.

The caveat

The voice examples ARE the skill. Without 5-10 of YOUR actual past listings in the prompt, the output reads identical to every other agent using the same shape of skill. Maintain a 'voice examples' file religiously - add new ones, retire stale ones, version it in git.

What you build

A Mac mini in your office running a local agent runtime, ffmpeg, and Whisper. A watched folder script triggers on new video files. The agent transcribes the audio (Whisper), generates a Reel script and social copy (Claude or local LLM), and uses ffmpeg to cut three vertical clips with auto-captions. All local, no SaaS, no monthly fee, your raw footage never leaves your office.

The stack

Mac mini (M-series, 16GB+) + OpenClaw or any local agent runtime + ffmpeg + openai/whisper or m-bain/whisperX + a folder-watching script (Python watchdog or fswatch).

Repos to study or fork

• openai/whisper

  OpenAI's open transcription model. Runs locally on a Mac, no API calls. The 'large-v3' model is what you want for accuracy; the 'medium' model is the speed-quality sweet spot.

• m-bain/whisperX

  Faster Whisper with proper timestamps and speaker diarization. If you're cutting clips programmatically, the precise timestamps from whisperX are what you'll align ffmpeg cuts against.

• FFmpeg/FFmpeg

  The command-line tool that does the actual video cutting and captioning. You won't read this source - you'll learn 8-10 specific ffmpeg invocations and copy-paste them forever. Lots of cookbook examples online.

How this adds value

For you

11 hours per week saved on manual editing. The same number FlexClip cites in their marketing - except you keep the workflow, the data, and the savings.

For your business

More content shipped per listing equals more impressions equals more inbound. Three vertical cuts plus a LinkedIn post per listing instead of one polished hero piece. The math compounds across your book.

For your clients

Their listing's video gets cut into 5-7 pieces of social content instead of one polished hero piece. More eyeballs per dollar they spent on media. Their listing moves faster because more buyers see it.

The caveat

Whisper hallucinates on quiet audio. Always review transcripts before publishing - it loves to invent words during silences. Also, your output is only as good as your input - a shaky iPhone walkthrough won't become a polished Reel just because AI touched it.