MBA to Commas: Reddit Homebuyer Concierge

An AI homebuyer concierge that lives in Reddit. Answers first-time-buyer questions across r/FirstTimeHomeBuyer and r/RealEstate using a licensed realtor's playbook. pros.

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About the project (Markdown for the big text box) Inspiration Every week I get the same DM: "I want to buy a house but I don't even know where to start. " I'm a licensed California realtor who has personally sold over $1.1M in property and run an Airbnb that has done $40K+ in bookings, and I'm also a content creator with 300M+ views and 93K+ followers. Across both audiences, the question is identical, and the answer is buried in scattered Reddit threads where the advice is wildly inconsistent. r/FirstTimeHomeBuyer alone has hundreds of posts a day, mostly answered by well-meaning strangers with no license, no skin in the game, and no follow-up. There is room for one trusted, transparent voice that always shows up with the same playbook, cites its sources, and hands the user off to a real human when it matters. That is what MBA to Commas: Reddit Homebuyer Concierge is. What it does The bot lives inside Reddit. It watches a curated set of homebuyer subreddits (r/FirstTimeHomeBuyer, r/RealEstate, r/MBA, r/personalfinance), detects new posts that match real homebuyer intent (down payment, FHA, escrow, closing costs, agent vs no-agent, rate buy-down, house hacking), and posts a clearly disclosed AI reply grounded in a licensed realtor's playbook. For high-intent posters, the bot offers a private DM with a vetted human realtor in their state. Every interaction logs to a dashboard so I can see what people are actually asking, where the bot is winning, and where humans need to take over. How I built it

  1. Reddit Devvit app that subscribes to a list of subreddits and listens for new posts.
  2. Intent classifier (Claude Haiku 4.5) decides whether a post is a real homebuyer question or just venting / off-topic. Cheap and fast at this volume.
  3. Knowledge base built from my own realtor playbook: California-specific examples, FHA vs conventional walkthroughs, escrow timeline, agent commission post-NAR-settlement reality, first-gen buyer framing. Stored in Pinecone for retrieval.
  4. Response generator (Claude Sonnet 4.6) writes the reply using retrieved knowledge plus a disclosure preamble: "Hi, I am an AI assistant trained on a licensed CA realtor's playbook. Here is what I would tell a friend asking this... "
  5. Routing layer detects high-intent signals (specific city, specific budget, specific timeline) and offers a DM with a vetted human pro.
  6. Dashboard in Next.js on Vercel, fed by a Postgres event log: posts seen, replies sent, upvotes earned, DMs initiated, conversions to human handoff.
  7. Rate limiting and subreddit allowlists so the bot only posts where mods have explicitly opted in. No drive-by spam. What I learned Reddit rewards honesty. Every prototype I tried that hid the AI identity got downvoted into oblivion within an hour. The moment I led with disclosure ("I am a bot, here is who built me, here is my source"), engagement flipped. A clearly-labeled AI that adds real value beats a disguised bot every single time. The other lesson: real estate is a state-by-state regulated business, and a national bot is a compliance landmine. Constraining the bot to California for v1 is a feature, not a bug. Challenges I ran into
  8. Reddit's AI content rules. Some subs ban AI replies outright. Built an allowlist + a manual mod-outreach process before posting anywhere new.
  9. Distinguishing intent from vent. "I will never afford a house in this market" is not a question. The classifier needed real tuning to avoid replying to despair posts.
  10. Compliance. I am a licensed realtor in California, not a mortgage broker, not a financial advisor. Every reply has to stop short of regulated advice. Wrote a hard-coded set of off-limits topics into the prompt.
  11. Latency. Initial version took 12 seconds to reply. People scroll past that. Restructured to detect, retrieve, and respond in under 3 seconds using a lighter classifier upstream. Accomplishments I am proud of Built end-to-end in 14 days, solo, while in the middle of an MBA semester at Berkeley Haas. Operator-grade knowledge base, not scraped advice from blog spam. Clear ethical line: the bot qualifies, humans close. The bot will never pretend to be a licensed agent in a transaction. Distribution baked in. The creator behind the bot already has 300M+ views and 93K+ followers in the exact under-35 housing-curious audience this serves. What is next Open the knowledge base to other licensed realtors so the bot can answer in their states. Add subreddits for renters (r/renting), refinancers, and house hackers. Subreddit-side widget: a Devvit post type that lets a homebuyer fill a 5-question intake and get a personalized response, without the public thread. Long-term, this becomes the front door for MBA to Commas: a creator-led, AI-native brokerage that meets first-time buyers where they actually are.

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