AI automation for brokers, agencies, and proptech platforms
Lead qualification, listing content, follow-up automation, and market analysis. $3,000/mo retainer for brokerages and proptech operators.
Who this is for
Broker, agency principal, or proptech operator where lead qualification is manual, listing descriptions are slow to write, and follow-up emails are inconsistent.
The pain today
- Lead qualification is manual and agents waste time on unqualified leads
- Listing descriptions are slow to write and often generic
- Email follow-ups are inconsistent across agents
- Market analysis and comps takes hours per deal
- CRM AI features are too generic for local-market nuance
The outcome you get
- AI automations for real estate on $3,000/mo retainer
- Lead qualification scoring that reflects your actual pipeline patterns
- Listing content generation with brand voice and local expertise
- Email follow-up sequences tailored to lead source and stage
- Integration with existing CRM (Follow Up Boss, kvCORE, BoomTown)
Real estate AI wins that actually pay back
Three deliver clear ROI. Lead qualification — LLM scores incoming leads based on source, initial behaviour, and enrichment data, flagging hot leads for immediate human contact. Cuts agent time on cold leads. Listing content — LLM drafts descriptions from property data plus local market knowledge, human agent reviews. Cuts listing prep time 60 to 80 percent. Follow-up sequences — personalised email sequences per lead source and stage, automated until a reply triggers human takeover. Each respects the agent's judgement while eliminating the repetitive typing and scoring work.
Lead triage and follow-up patterns
Lead qualification pulls signals from lead source (Zillow, Realtor.com, direct, referral), initial form content (timeline, price range, financing), enrichment (email domain, LinkedIn presence, previous activity), and behavioural data (emails opened, listings viewed). LLM scores and categorises. Hot leads route to agents immediately with suggested first-call talking points. Warm leads get automated nurture sequences. Cold leads get minimal automation. The scoring model tunes over time based on which leads actually convert.
Listing-content generation guardrails
Generic AI listing copy sounds generic. The fix: agent-level brand voice prompts, local market knowledge in the prompt context, structured property data feeding facts. Agent reviews every draft before publish. For agents writing 10+ listings per month, this saves hours weekly. For boutique brokerages with a specific voice, prompt tuning captures it over 1 to 2 months. Generic MLS auto-generated descriptions are not this — they are worse than hand-writing. Properly tuned AI listing content, with human review, is competitive with senior copywriter output.
Pricing and engagement model
$3,000/mo retainer. Covers AI integration, prompt engineering, CRM integration, monitoring, iteration. 14-day money-back guarantee. Cancel anytime. 100 percent code ownership under Work Made for Hire. LLM costs pass through — typically $100 to $500/month for mid-size brokerages. For boutique brokerages under 10 agents, the retainer often pays back inside the first quarter through agent time saved. For larger brokerages, broader rollout lifts ROI proportionally.
Case: Imohub and Instill
Imohub: 120,000+ property portal with sub-500ms queries and 70 percent infra savings (Next.js, React, Laravel, MongoDB, Meilisearch, AWS, Docker). Performance discipline applies to AI — real estate AI needs to be fast and cost-controlled. Instill: self-initiated AI skills platform with structured-prompt library (Next.js 16, React 19, TypeScript, PostgreSQL, Vercel, MCP Protocol). The structured-prompt pattern transfers directly to real estate — listing prompts, follow-up templates, qualification frameworks as structured artifacts agents iterate on.
When a CRM's built-in AI is enough
Follow Up Boss, kvCORE, BoomTown, and LionDesk all ship basic AI (smart auto-responses, lead scoring). For brokerages under 20 agents with simple needs, platform AI may be enough. Custom retainer pays back when brokerages need deeper integration, local-market nuance, or AI features the platform does not offer — especially around listing content or boutique-branded follow-ups. I help evaluate in week one. Often the honest answer is 'your CRM already does 60 percent of what you want — turn it on properly before adding custom.'
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.
Frequently asked questions
The questions prospects ask before they book.
- Can you integrate with Follow Up Boss or kvCORE?
- Yes. Follow Up Boss API, kvCORE, BoomTown, LionDesk — all integrate via API or webhook. Lead qualification scores push into CRM fields. Listing content generates and writes back to CRM and MLS. Follow-up sequences orchestrated through CRM email tools. Initial integration typically 2 to 3 weeks. For brokerages on custom CRMs, direct API or middleware integration.
- How do you handle MLS data in AI prompts?
- MLS data feeds AI prompts as structured facts (bedrooms, bathrooms, square footage, neighbourhood). AI cannot invent property facts that do not exist in the source data. Description generation combines MLS structured data with agent-provided local knowledge. Output reviewed before publish to MLS or site. No MLS rule violations — the AI does not scrape or redistribute MLS data beyond what your licence allows.
- What about compliance for copy and fair-housing?
- Fair-housing compliance is mandatory. System prompts explicitly exclude protected-class references (race, religion, familial status, national origin, etc). Output filters catch violations before publish. Agent reviews every listing before publishing. For brokerages in strict-compliance markets (California, New York), additional review layers. Fair-housing violations kill brokerages; AI has to be built with hard rules, not hopeful guidelines.
- How much does it cost per listing?
- Typical: $0.05 to $0.30 per listing description generated, depending on model choice and description length. For a brokerage doing 100 listings per month, AI cost around $5 to $30/month on top of the $3,000 retainer. Agent time saved per listing: 15 to 30 minutes. ROI is obvious once usage scales.
- Can we use AI for cold prospecting?
- Yes, but carefully. Cold outreach via AI-generated email is high-risk — recipients flag as spam, deliverability drops for the whole domain. What works: AI-drafted templates that agents personalise before sending, or nurture sequences to already-opted-in leads. Agent approval on every cold message for the first 3 months. Scaling fully automated cold email is rarely worth the deliverability risk. I push back on this pattern when clients ask.
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