The outcomes teams like yours see with Tars
Purpose-built AI agents for real estate
Customer Support
Answer every buyer, seller, and tenant in seconds, and keep your agents for the deals that need them.
Answer listing, pricing, and neighborhood questions instantly
The agent fields the questions every listing generates: the asking price, square footage, the year built, HOA fees, school zones, what's nearby, and whether the property is still available. It answers from your own listing data and brokerage information, not a guess off the internet.
It works on web, WhatsApp, and SMS, reads your listing and CRM data so every answer reflects the current price and status, and stays inside the question the buyer actually asked instead of jumping topics mid-conversation.
Buyers get a real answer at the moment of interest, day or night, and your agents stop fielding the same listing questions over and over.
Keep buyers and sellers updated on transaction status
Once a deal is in motion, the agent answers the status questions that fill an agent's phone: where the offer stands, whether the inspection is scheduled, what's outstanding before closing, and what the next step is.
It pulls status from your transaction and CRM systems and proactively updates both sides on the channel they prefer, and hands off to the agent or transaction coordinator with the full thread when a question needs a person.
Clients feel informed through the most stressful part of the deal, and your agents spend less time on "any update?" calls and more time closing.
Take tenant requests and maintenance intake off the phones
For property managers, the agent runs tenant intake end to end: a maintenance request with the unit, the issue, the urgency, and a photo, a question about rent or the lease, or a request to renew. It captures the details the same way every time so nothing gets lost in a voicemail.
It works around the clock on web, WhatsApp, and SMS, writes the request to your property management system, and routes emergencies to on-call staff immediately while logging routine requests for the queue.
Tenants get an instant acknowledgment instead of a missed call, and your team works from clean, complete requests instead of chasing details.
Route hot inquiries to the right agent with full context
Not every inquiry should land in the same shared inbox. The agent reads what the buyer or seller wants, a specific listing, a neighborhood, a price range, a listing appointment, and routes the qualified inquiry to the right agent or team.
Because it's one continuous thread, the agent opens the handoff already knowing who the lead is, what they asked, and what the AI agent already answered, instead of starting cold. A high-intent lead reaches a person while they're still engaged, not an hour later.
Fewer leads go cold in a shared inbox, and your agents spend their time on the inquiries that are ready to move.
Customer Acquisition
Turn every property inquiry into a qualified, ready-to-work lead, around the clock.
Capture and qualify property leads around the clock
The agent greets every visitor from a listing page, a Facebook ad, or a WhatsApp number and qualifies them the way your best agent would: are they buying or selling, what's their budget and timeline, which area, and are they working with an agent already. Leads arrive at all hours, and the agent never misses one.
It works across web, WhatsApp, SMS, and email, asks the qualifying questions in a natural two-way conversation instead of a static form, and writes the qualified lead and its answers straight into your CRM so an agent picks up a warm, ready file.
Save Max Canada used a Tars agent to capture 1000s of leads and reach a 62% response rate from people who would otherwise have left without a trace.
Match buyers to listings in the conversation
Instead of sending a buyer off to scroll a search page, the agent asks what they're looking for, the area, the price range, beds and baths, must-haves, and surfaces the listings that fit right there in the chat.
It reads your live listing and MLS data so it only shows what's actually available and priced correctly, and books the next step, a viewing or an agent call, the moment a buyer likes a property.
Buyers see relevant homes faster, and your agents inherit a lead who has already told you exactly what they want.
Schedule viewings without phone tag
The agent handles viewing requests end to end: it offers the open slots, confirms the property and time, collects the buyer's details, and books it on the agent's calendar, no back-and-forth voicemails.
It works on the channel the buyer started on, syncs to your calendar and CRM, and sends reminders so the showing actually happens, with reschedules handled in the same thread.
More showings get booked while interest is hot, fewer no-shows, and your agents walk into viewings with a buyer who is already qualified.
Pre-qualify buyers and collect documents for approval
The agent runs the conversation a financing-ready buyer actually starts with: it qualifies purpose, budget, and timeline, then guides the buyer through what a pre-approval needs, income, down payment, and the documents to upload for their situation. It asks the same questions your best loan partner would.
Document collection and the pre-qualification intake run as deterministic steps, so every buyer is taken through the same sequence and nothing is missed, with the completed file written to your CRM or your lending partner's system.
More buyers reach an agent already pre-qualified and document-ready, so the deals in your pipeline are the ones that can actually close.
How Save Max Canada turned property inquiries into 1000s of leads
Save Max, one of Canada's largest real estate brokerages, was getting interest from across its listings and marketing, but a static form and business-hours follow-up let too many of those inquiries slip away. Save Max deployed a Tars AI agent to capture and qualify every inquiry in a conversation, around the clock, on the channels buyers and sellers actually use, asking the right qualifying questions and recording each lead instead of letting it go cold. The result: 1000s of leads captured and a 62% response rate, turning passive traffic into a pipeline its agents could actually work.
One conversation per buyer, across every channel and straight to the right agent.
A buyer asks about a listing on your website at lunch, replies on WhatsApp after work, and confirms a viewing by SMS. In Tars that is one conversation, not three disconnected leads. The channel is just where each message arrived. There is no separate live-chat tool either. Your AI agent and your real estate agents work in the same thread. The agent captures, qualifies, and answers what it can, and when a lead is hot or a question needs a person, it hands over with the whole story attached: who the buyer is, what they want, their budget and timeline, and what's already been answered. Your agent picks up the lead already knowing all of it. Your buyer never repeats themselves. And the high-intent inquiry reaches a person while they're still interested, not after the lead has gone cold.
How Tars Agents Get Better
The Tars agent flywheel
Standing up an agent your customers trust isn't a click a button and you're done story. Tars closes the loop end to end: train, test, deploy, learn, improve. More conversations get resolved instantly, and fewer reach your team, with every interaction.
Train
Connect your knowledge base, past conversations, and the systems your team already uses. The agent learns your products, your policies, and your customers, configured to your own data and rules.
Test
Simulate the agent against real customer questions before launch. Failure modes become validated evaluators, so you see real accuracy before a single customer sees it.
Deploy
Go live on web, WhatsApp, SMS, and email when the numbers say it's ready, with code based and LLM as judge evaluators scoring every conversation.
Get Insights
See which questions the agent struggles with, why escalations happen, and where customers drop off, with resolution broken down by use case.
Improve continuously
Close the gaps, re test, and raise resolution month over month. Each loop resolves more and escalates less.
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