Lead qualification in under 5 minutes, listing content at scale, and follow-up sequences that run without an inside sales agent. $3,999/mo retainer.
- Analyze
- Automate
- Monitor
monthly retainer
Who this is for
Broker-owner, agency principal, or proptech operator where leads go cold because no one gets back fast enough, listing descriptions eat an agent's afternoon, and follow-up consistency varies by who's working that day.
The pain today
- Leads from Zillow and Realtor.com go cold while agents are on other calls
- Listing descriptions take 30–45 minutes each and still sound generic
- Follow-up quality depends on which agent is handling the lead that day
- CRM AI features don't understand your local market or brand voice
- Market comps and deal prep eat hours that should go to customer relationships
The outcome you get
- Lead qualification firing within minutes of inquiry, 24 hours a day
- Listing content that matches your brand voice and local market knowledge
- Consistent follow-up sequences tailored to lead source and pipeline stage
- CRM integration with Follow Up Boss, kvCORE, BoomTown, or your custom stack
- Agent hours redirected from admin work to closings and customer relationships
The three real estate AI wins that pay back
I've built AI automation across industries, and real estate has three problems where custom AI pays for itself faster than anywhere else.
Lead qualification: an LLM scores every incoming lead within minutes based on source (Zillow, Realtor.com, direct, referral), form content (timeline, price range, financing), enrichment data, and behavioral signals. Hot leads route to agents immediately with suggested talking points. Warm leads enter nurture sequences. Cold leads get minimal automation. The model tunes over time as you feed it conversion outcomes.
Listing content: the LLM drafts descriptions from structured property data plus local market context loaded into the prompt. An agent reviews before publish. For someone writing 10 or more listings a month, this recovers hours every week.
Follow-up sequences: personalized emails triggered by lead source and stage, automated until a reply hands off to a human. Each sequence respects the agent's judgment while eliminating the repetitive typing.
Lead qualification firing within minutes of inquiry, 24 hours a day
Why response speed is the real estate variable nobody automates
There's a well-documented pattern in real estate lead conversion: the first agent to respond wins at a rate that makes everything else secondary. Leads that don't hear back quickly don't wait. They fill out the next form.
Most brokerages know this and try to solve it with staffing — an inside sales agent monitoring the inbox, or an on-call rotation. Both are expensive and inconsistent outside business hours.
AI qualification solves the 24-hour coverage problem without the headcount. An LLM-powered agent engages the inquiry immediately, asks qualifying questions through email or SMS, scores the lead, and pings the right human with full context. The agent shows up to the call already knowing the buyer's timeline, price range, and financing situation. That's a different conversation than a cold callback.
120k+: Properties indexed and searchable.
Listing-content generation with guardrails
Generic AI listing copy sounds generic because generic prompts produce generic output. The fix has three parts: agent-level brand voice in the prompt, local market knowledge loaded as context, and structured property facts feeding the generation.
Every draft gets agent review before it goes anywhere near an MLS or website. Fair-housing compliance is hardwired into the system prompts — protected-class references are excluded at the instruction level, and output filters catch anything that slips through. For brokerages in strict-compliance markets, additional review layers are easy to add.
For a boutique brokerage with a specific voice, prompt tuning captures it over the first month or two. The output after that is competitive with a senior copywriter who knows your market. For agents doing 10+ listings per month, that's hours recovered every week.
CRM integration: connecting AI to where agents already work
The part that makes or breaks real estate AI is integration with the tool agents actually open every morning. Lead scores that sit in a separate dashboard nobody checks deliver zero ROI.
I build the connections: lead qualification scores push into CRM fields in Follow Up Boss, kvCORE, BoomTown, or LionDesk. Listing content generates and writes back to CRM and MLS. Follow-up sequences orchestrate through the CRM's email tools so agents see everything in one place.
Initial integration takes 2 to 3 weeks for standard platforms. For brokerages on custom CRMs or heavily configured instances, direct API or middleware connections. The goal is AI outputs that appear inside the workflow agents already have — not another tab to manage.
Real estate background: Imohub
When I served as CTO at Imóveis SC, I rebuilt the property portal as Imohub: 120,000+ listings, sub-500ms search queries, and 70% infrastructure cost savings using Next.js, React, Laravel, MongoDB, Meilisearch, AWS, and Docker.
The lessons that transferred directly to real estate AI: property data needs to be fast and structured for AI prompts to produce useful output, cost discipline on the AI layer matters as much as cost discipline on the infrastructure layer, and search quality depends on the data model as much as the search engine.
Imohub ended up in the top 3 Google rankings for its core terms. Prompt quality and data structure are the same type of problem.
When platform AI is enough — and when it isn't
Follow Up Boss, kvCORE, BoomTown, and LionDesk all ship basic AI now: smart auto-responses, rudimentary lead scoring, drip campaign builders. For a brokerage under 20 agents with straightforward needs, turning that on properly may cover 60 percent of what they want.
I say this directly in week one. If your CRM already does most of what you need, the honest answer is to configure it correctly before paying for custom automation.
Custom work pays back when the brokerage needs local-market nuance the platform can't be trained on, listing content with a specific branded voice, lead scoring that reflects your actual pipeline patterns, or AI features the platform simply doesn't offer. Larger brokerages also see ROI scale proportionally once the same automation touches more volume.
Recent proof
A comparable engagement, delivered and documented.
Rebuilt a real estate portal at a fraction of the cost
Rebuilt Imóveis SC's real estate portal as ImoHub, a faster, more scalable successor, handling 120k+ properties with sub-second search and drastically reduced AWS costs.
Read the case studyFrequently asked questions
The questions prospects ask before they book.
The system engages within minutes of the inquiry arriving — not hours. An LLM-powered agent sends a qualifying message via email or SMS, collects timeline, budget, and financing details, and scores the lead before routing it to the right agent with full context. That means the agent's first call is a warm handoff, not a cold callback to someone who may have already moved on.
Yes. Follow Up Boss, kvCORE, BoomTown, and LionDesk all integrate via API or webhook. Lead qualification scores push directly into CRM contact fields. Listing content generates and writes back to CRM and MLS records. Follow-up sequences run through the CRM's own email tooling so agents see everything in one place. Standard platform integrations take 2 to 3 weeks. Custom or heavily configured CRMs take a bit longer.
Fair-housing compliance is built into the system at the instruction level, not bolted on as an afterthought. System prompts explicitly exclude protected-class references — race, religion, familial status, national origin, and others. Output filters catch anything that slips through before a draft reaches an agent. Every listing still goes through agent review before publishing. For brokerages in strict-compliance markets, additional review layers are straightforward to add.
Typical LLM cost is $0.05 to $0.30 per listing description, depending on model choice and length. For a brokerage producing 100 listings per month, that's roughly $5 to $30 in AI costs on top of the $3,999 retainer. Agent time saved per listing is 15 to 30 minutes. At any reasonable volume, the math is straightforward.
MLS data feeds the AI as structured facts: bedrooms, bathrooms, square footage, neighborhood, amenities. The model generates descriptions from those facts — it doesn't scrape, redistribute, or infer property details that aren't in the source data. Output is reviewed before it goes to MLS or any public surface. The workflow stays within whatever your MLS data license permits.
Platform AI covers the basics well. Where it falls short is local-market nuance — a generic model doesn't know your neighborhood-level pricing patterns or your brand's voice. Custom automation also lets lead scoring tune over time based on your own conversion data, not industry averages. For brokerages with a specific positioning or above-average listing volume, the gap between platform AI and purpose-built automation is noticeable in conversion rates within the first quarter.