B2B Real Estate AI Search Agent with Claude Code

Multiple subreddits posted the same build in 2026: a B2B real estate AI search agent built in two days with Claude Code. The build is simple if you pick the right data layer. Direct MLS access is gated and expensive, but public SERP plus Reddit covers 80% of the same signal at a fraction of the cost.

The Job to Be Done

B2B sales teams selling into real estate brokerages need fresh signals: which brokerages are hiring, which markets are heating up, which agents are switching firms, which brokerages are getting bad reviews. The agent surfaces the highest-intent prospects every morning.

Why MLS Access Is the Wrong Path

Direct MLS access requires sponsorship from a licensed broker, costs $300 to $1,000 per month per market, and limits the data to active listings. The B2B sales job needs broader signal: hiring, news, sentiment. SERP plus Reddit covers the broader signal.

The Two-Day Build

Day one: Claude Code skill that takes a city plus practice area and returns top 25 brokerage prospects from SERP. Day two: add Reddit signal, contact extraction via Scavio's extract endpoint, and a morning email digest.

The Day One Skill

# brokerage-discovery.md
Inputs: city, practice (residential|commercial|industrial)
Steps:
1. Scavio SERP query: "{practice} brokerages {city} 2026 hiring OR new listings"
2. Top 25 organic results returned
3. For each result, classify by relevance: brokerage homepage, news article,
   listing page, agent profile.
Output: markdown list of prospects with one-line context per item.

The Day Two Reddit Signal

Reddit threads about brokerages are surprisingly informative. r/ RealEstate, r/realtors, r/CommercialRealEstate carry hiring posts, complaints about specific brokerages, and market commentary. The agent searches the brand name plus the city for recent threads.

import os, requests

API_KEY = os.environ["SCAVIO_API_KEY"]
H = {"x-api-key": API_KEY}

def reddit_signal(brokerage_name, city):
    r = requests.post("https://api.scavio.dev/api/v1/reddit/search",
        headers=H, json={"query": f'"{brokerage_name}" {city}'}).json()
    return r.get("posts", [])[:10]

def hiring_signal(city, practice):
    r = requests.post("https://api.scavio.dev/api/v1/reddit/search",
        headers=H, json={"query": f"{city} {practice} brokerage hiring 2026"}).json()
    return r.get("posts", [])[:10]

The Contact Extraction Step

For each brokerage homepage, run Scavio's extract endpoint to pull the contact-info block. Most real estate sites have predictable layouts. The extracted markdown contains phone numbers and contact URLs in regex-friendly form.

The Scoring Logic

Score each prospect on three signals: SERP recency (was the brokerage in news in the last 30 days), Reddit activity (positive or negative mentions), and contact availability (did extract return usable contact info). Multiply for a single score; sort descending.

The Morning Email Digest

Top 25 prospects with the score, the SERP context, the Reddit signal one-liner, and the contact info. The SDR opens the email at 7 AM, reads through 25 prospects in 10 minutes, and starts outreach. The agent runs the discovery work overnight.

Why Claude Code Is the Right Orchestrator

Skills are the right unit of work. Each step (discovery, signal, extract, score, email) is a markdown file. Easy to read, easy to modify, easy to compose. The agent invokes them in sequence with one Claude prompt per morning run.

The Cost Profile

Per-market run consumes about 75 Scavio credits ($0.225 at fast tier). At one market run per day for 20 markets, total spend is about $5/mo. Compare to $1K to $3K/mo for paid prospecting tools that cover the same job.

What This Cannot Do

It cannot replace local market knowledge. It cannot match the depth of an MLS direct feed for active-listing data. It cannot write the outreach for you (you should write your own openers based on the signal). It is a discovery and signal layer, not an end-to-end autopilot.

The Two-Day Build in Numbers

Sixteen hours of focused Claude Code time. Five markdown skills. One Postgres table for prospects. One cron entry for the morning run. One SMTP integration for the digest. Live the next morning.