Listing inquiries replied within minutes, not hours.
Reads each inbound listing inquiry, shares the right comps, proposes private tour times, and adds the lead to your CRM with full context for follow-up.
14 inquiries handled today. Sample reply:
How does OpenAI (ChatGPT) work for real estate teams?
OpenAI (ChatGPT) works for real estate teams as the engine behind an Arahi AI agent built around the workflows that actually consume your week. The agent reads context from OpenAI (ChatGPT) and the other systems your real estate operation depends on, runs the routine work in the background, and surfaces only the cases that need a human decision. Automate repetitive tasks and free up your real estate team to focus on high-value strategic work. Teams typically see rapid response to new leads once the agent is in production. Setup is no-code, every action is auditable, and the agent is scoped to the rules your real estate team defines — not a generic template applied to your business.
Built in plain English.
You write the rule the way you'd describe it to a teammate. The agent reads the rule, breaks it into the actions it'll take, and confirms the apps it'll touch — before it does anything.
- 1Read the trigger event and pull the contact's context
- 2Draft the message in your team's voice
- 3Cite each personalized line's source
- 4Queue for your review or auto-send by confidence
Get started in three steps
Connect OpenAI (ChatGPT)
Authorize OpenAI (ChatGPT) in your Arahi AI dashboard. The secure connection takes less than 60 seconds.
Configure Your AI Agent
Set up triggers, actions, and conditions specific to how your team uses OpenAI (ChatGPT). For real estate teams, this typically means routing workflows from tools like MLS alongside OpenAI (ChatGPT).
Deploy & Monitor Results
Your AI agent goes live immediately. Track tasks automated, time saved, and accuracy metrics in real-time.
418 Linden · 3-bed Craftsman · two tour times
Glad you found 418 Linden. Two recent comps in the same school district: 622 Beech sold $945K (3/2, 1,820 sf, mid-Feb) and 311 Oak $898K (3/1.5, 1,650 sf, late Jan). 418 Linden is listed at $920K and shows like the Beech comp.
Personalized using LinkedIn activity from the last 30 days.
Approve before it sends.
Every draft lands in a review queue. You approve, edit, or reject — the agent never acts on its own unless you explicitly turn that on for a workflow you trust.
Every action, with the reasoning attached.
Each step the agent takes is logged with what it did, why it did it, and which app it touched. Audit-ready, so security and compliance can sign off without backfilling.
- Marco11:42 AM
Approved the draft to jordan.kim@homemail.com.
- Agent11:41 AM
Drafted the email and queued it for review.
Reason: High-confidence personalization but recipient is C-level — escalating per policy.
- Agent11:40 AM
Pulled LinkedIn activity and HubSpot deal context.
- Agent11:40 AM
Triggered: Reply to every listing inquiry with @ChatGPT — share the right comps, propose a
- Agent11:38 AM
Confirmed sender domain DKIM is healthy.
Frequently asked questions
Follow Up Boss, Sierra Interactive, kvCORE, BoomTown, Chime, plus direct MLS integration via your local board. Listings sync automatically.
From the local MLS, with the criteria you'd actually use (school district, sq ft range, recent sale window). Comps come with rationale, not just a list.
Yes — it checks your calendar, the listing agent's availability, and the buyer's schedule, then proposes private tour times.
It manages your past-client touchpoints (purchase anniversaries, market updates) and personalizes outreach for referrals.
Yes — different leverage on data sources (CoStar, LoopNet) and different inquiry handling, but the same engine.
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