Every Buyer Gets a Personalized Shortlist Before the Listing Hits the Portal
3x faster buyer-property matching, 40% increase in viewing-to-offer rate, zero missed listings in buyer search areas
The problem
Property matching is the core value proposition of any real estate agency, yet it remains one of the most manually intensive tasks. A buyer says they want three bedrooms, good schools, under $650K, and walkable to transit. The agent mentally scans their memory and the MLS database for matches. The best agents carry hundreds of listings in their head. The rest miss opportunities daily.
The challenge intensifies in fast-moving markets. New listings appear hourly, buyer requirements shift after showings, and inventory turns over in days. An agent who checked the MLS on Monday morning may miss a perfect-fit listing that appeared Monday afternoon. Meanwhile, the buyer finds it on Zillow and goes direct to the listing agent, cutting your agency out entirely. In a market where days on market averages 15-20 days for desirable properties, a 24-hour delay in matching means the property is already under contract.
Beyond speed, there is a nuance problem. Buyers describe what they want in imprecise terms. "Updated kitchen" means different things to different people. "Good neighborhood" might mean walkability score, school ratings, or crime stats. Human agents develop intuition for these preferences over time through showing feedback, but that intuition does not scale across a database of thousands of listings and hundreds of active buyers.
Emma is your AI Property Matching specialist. She processes the full MLS inventory continuously, understands semantic preferences beyond simple filters, learns from showing feedback ("loved the open layout, hated the busy street"), generates Comparative Market Analysis data for pricing context, and proactively surfaces matches the moment they hit the MLS -- often before the buyer has seen the portal listing. At 7 AM, your agents see: "3 new matches for the Chen family: 142 Oak St (94% fit -- open layout, walkable to Lincoln Elementary, $625K). [Send to Buyer] [Schedule Showing] [Skip]."
How it works
How Emma works, step by step
Each step is automated. Emma only escalates when human judgment is required.
Emma parses requirements into structured criteria including hard filters (budget, bedrooms, location radius, school district) and soft preferences (style, lot size, walkability, renovation tolerance, commute time). She also ingests any showing feedback from previous properties
Emma runs matching algorithm against all active buyer profiles, scoring each match on fit percentage with weighted criteria. Each match includes a personalized note explaining why this property fits the buyer -- referencing their specific stated preferences and showing feedback
Emma sends the matched property to the assigned agent via Slack with buyer context, CMA data (comparable recent sales, price per square foot, days on market trend), and recommended talking points. Agent can [Send to Buyer], [Schedule Showing], or [Skip with Reason]
Emma refines the buyer preference model based on feedback patterns -- adjusting future match scoring weights. "Loved the open layout" increases weight on floor plan openness. "Street was too busy" decreases tolerance for high-traffic locations. The model improves with every showing
Emma generates a weekly market summary for each active buyer: new listings in their search area, price changes on previously matched properties, days-on-market trends, and any off-market opportunities from the agency's own listings
Emma escalates to the assigned agent with full analysis before any buyer contact. Investment properties include cap rate calculation, estimated rental income, and comparable investment sales
What Emma handles vs. what stays with you
Clear boundaries. Emma works autonomously within defined limits and escalates everything else.
- ✓ Emma parses requirements into structured criteria including hard filters (bud...
- ✓ Emma runs matching algorithm against all active buyer profiles, scoring each ...
- ✓ Emma sends the matched property to the assigned agent via Slack with buyer co...
- ✓ Emma refines the buyer preference model based on feedback patterns -- adjusti...
- ■ Off-market or pocket listing introductions require agent approval before buyer contact
- ■ All showing arrangements are confirmed by the human agent
- ■ Pricing guidance, market opinions, and investment advice are provided only by licensed agents
- ■ Emma does not share confidential seller information (motivation, bottom-line price) with buyers
- ■ Properties with development potential, zoning issues, or title complications are flagged for agent expertise
Integrations
Works inside your existing tools
Emma connects to the platforms you already use. No new software to learn.
Implementation
From zero to Emma
Emma is deployed gradually with measurable checkpoints at every stage.
- ✓ Full MLS data feed with property attributes, photos, and status updates
- ✓ Historical buyer-property match data with conversion outcomes (showings, offers, closes)
- ✓ Buyer feedback records from past showings
- ✓ Agent notes and preference interpretations from buyer consultations
- ✓ Geographic, school district, and walkability score data
Pilot runs with 50 active buyer profiles across two property types (e.g., single-family homes and condos). Week 1-2 Emma generates matches in parallel with human agents for the same buyers.
Your AI team
Works alongside Emma
These AI employees share data and coordinate with Emma to cover your full operation.
Deploy Emma for your real estate operations
Start with a 90-minute discovery session. We will assess whether Emma is the right fit for your workflows and show you exactly what changes.