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Illustrative example

Real Estate · Anaheim, CA

An AI leasing assistant that answers at 2 a.m. and never loses a lead

An example build for a property-management firm

A property-management firm missing half its leasing inquiries after hours. We build an AI leasing assistant grounded in live unit data — answering in 60 seconds, booking tours, and lifting signed leases.

Duration~8 weeks

What a build like this targets

What a build like this targets

response to every inquiry, 24/7
<60s
answers from live unit data — never invented
Grounded
after-hours coverage that stops losing leads overnight
24/7
escalation on every decision that matters
Human
Where it started

The challenge

Picture leasing teams handling inquiries across phone, email, and half a dozen listing platforms. After-hours messages — more than half the total — wait until morning, and hot leads book tours with faster competitors overnight.

Off-the-shelf chatbots have already failed here: they can't answer property-specific questions (pet policies, actual availability, real pricing) and agents distrust anything that touches their pipeline.

How we worked

The approach

  1. A build like this grounds the assistant in live property data — real units, real availability, real policies — via the property-management system's API, so every answer is checkable, never invented.

  2. Trust is the adoption strategy: the assistant drafts and books, but agents see full transcripts, can take over any conversation live, and every automated action is logged and reversible.

The build

What we build

The scope a build like this ships — and with 714software, the whole thing is delivered before you pay a cent: no deposits, no change orders, no surprise line items.

  • AI assistant answering across web chat, SMS, and email — grounded in live unit data
  • Direct tour scheduling into agent calendars with smart routing by property
  • Automatic lead qualification and enrichment written back into the CRM
  • Escalation rules: pricing negotiations and edge cases hand off to humans with context
  • Eval suite scoring answer accuracy weekly against a golden set of real questions

Technology stack

Under the hood

Claude API
Next.js
PostgreSQL
pgvector
Twilio
AWS

Have a project in mind?

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