AI Listing Agent · New

Edensign AI Listing Agent

Know which design sells before you stage the first room.

Upload your listing photos, drop an address, and the AI Listing Agent returns a Fannie Mae UAD-calibrated home report, hyper-local market intelligence with a chat-based research agent, and a market-driven listing description — in one workflow.

UAD-calibrated home reportAddress-level market data
edensign · listing-agent · 14 photos → 8 rooms detected
Living room photo classified by the AI listing agentLiving Room · Q4 · C3
Primary bedroom photo classified by the AI listing agentPrimary Bed · Q4 · C4
Kitchen photo classified by the AI listing agentKitchen · Q3 · C3
Home report draft:UAD Q3.5 · 3 suggested improvements

Confían en nosotros

Why Edensign Listing Agent

One workflow. Photos in, listing-ready out.

Drop in your photos and an address. The agent classifies rooms, scores them on the Fannie Mae UAD scale, pulls live market data for your block, and writes a description tailored to local buyers.

Address · ZIP · Neighborhood
01

Market dashboard + AI assistant.

Drop an address (or ZIP) and the agent assembles pricing, comps, walkability, demographics and recent sales into a live dashboard. An AI assistant sits on top of it — ask follow-up questions, request explanations of any number, and dig deeper without leaving the page.

Dashboard · Live data · AI Q&A
Room classification preview
02

UAD-calibrated home report.

Vision AI categorizes every photo by room, de-duplicates angles of the same space, and scores each room on Fannie Mae's UAD quality and condition scale — with ranked improvement suggestions that move the property up a grade.

UAD Q/C ratings · Improvement plan
Generic
ChatGPT
Edensign
Template
Agent
Manual
03

Market-driven listing description.

Every description is grounded in what's actually selling in your market — the buyer archetype, the comps, the features local buyers are paying for. Not boilerplate. MLS-ready in one click.

Hyper-local · MLS-ready · One click

How it works

Photos + address in. Listing-ready out.

Four steps from a folder of raw photos to a Home Report, market brief, and listing description — same workflow your team already runs, automated end to end.

  1. i.

    Upload photos and an address

    Drag in a folder of listing photos and type an address or ZIP. That's the minimum — details like beds, baths and price are optional and just sharpen the output.

  2. ii.

    Live market dashboard — with an AI assistant on top

    You get a real dashboard: pricing, comps, walkability, buyer archetype, recent sales. An AI assistant sits on top of it — ask for explanations, run "what if" questions, or drill into any number without leaving the page.

  3. iii.

    Room categorization + UAD Home Report

    Vision AI sorts photos by room, collapses duplicate angles of the same space, and grades every room on Fannie Mae's UAD quality and condition scale — with prioritized improvement suggestions.

  4. iv.

    Market-driven listing description

    The agent writes an MLS-ready listing description tuned to the local buyer archetype and the strongest features in your photos. Copy, edit, and ship.

Home Report· 14 photos → 8 rooms · UAD Q3.5
01
Living Room
Hardwood · Bay window · Updated trim
Q4
quality
C3
condition
02
Kitchen
Stock cabinets · Laminate counter
Q3
quality
C3
condition
03
Primary Bedroom
Walk-in closet · Recessed lights
Q4
quality
C4
condition
04
Primary Bath
Original tile · Pedestal sink
Q3
quality
C2
condition
Suggested improvements · ranked by ROI
Refinish primary bath (C2 → C3) is the highest-ROI fix — estimated $2.4k spend, ~3% list-price lift in this submarket. Painting the kitchen cabinets is the cheapest grade bump (Q3 → Q3.5). The living room is already pulling above the comp set; lead with it in your hero photo.

What you get

Four artifacts. One workflow.

Each run produces a live market dashboard with an AI assistant on top, a UAD-calibrated home report, clean room-by-room classification, and a market-driven listing description — all ready to drop into MLS.

Market dashboard · AI agent assistant

Live dataAI Q&A
$618
Median $/sqft
18d
Median DOM
+4.2%
YoY
You
Why is YoY +4.2% here but the metro is flat? Who's actually buying on this block?
Edensign Assistant
The dashboard's +4.2% is driven by a tight comp set (n=14) skewed toward 2–3BR condos — the segment up most in this metro. Buyers here are ~62% young professionals and small families. Want me to pull the last 6 closed comps with their pricing context?

Home Report · Fannie Mae UAD

8 rooms3 improvements
Overall grade
Q3.5

Above average for the comp set. Two condition fixes would bump this to Q4.

Living RoomHardwood · Bay window · Crown molding
Q4C3
KitchenStock cabinets · Laminate counter
Q3C3
Primary BedroomWalk-in closet · Recessed lighting
Q4C4
Primary BathOriginal tile · Pedestal sink
Q3C2

Photo → room classification

14 photos8 unique rooms
Living · 1/2Living · 1/2
Living · 2/2⨯ dupLiving · 2/2
Primary BedPrimary Bed
KitchenKitchen
DiningDining
Primary BathPrimary Bath

Detected 8 unique rooms across 14 photos. Two duplicate angles of the living room were collapsed; the highest-quality shot of each room flows into the report and hero photo.

Listing description · market-driven

MLS-readyBuyer-tuned

Sun-drenched bay window. Quarter-sawn hardwood underfoot. A 1920s living room that has been loved, lived in, and quietly updated for the way young professionals actually use a home today.

Three bedrooms, two baths, and a walk-in primary closet that punches well above its square footage. The kitchen is honest — stock cabinets, laminate counter — but it's bright, functional, and leaves room for the buyer to add their own stamp without a gut renovation.

Five-minute walk to the T. Walk Score 91. Coffee, groceries and the park all within four blocks. Two recent comps within 500 feet closed at $612 and $625 per sqft — above the neighborhood median, in under three weeks.

412 words · Reading level 7 · Local archetype: young_professional

From the field

From a folder of photos to listing-ready, in one sitting.

"

I used to spend an hour pulling comps and another hour writing the listing copy. Now I drop in 12 photos and an address, the agent gives me a UAD-graded report plus a description that sounds like me — I review and ship.

Sarah Mitchell

Real Estate Agent · Seattle, WA

"

The home report is the unlock. Sellers see a UAD grade with three ranked improvements and an ROI estimate — the conversation about painting cabinets or refinishing tile suddenly has numbers behind it.

Raj Patel

Brokerage Lead · Austin, TX

"

Being able to chat with the market data is the part my team didn't know they wanted. "What did this floor plan close at last year?" — answered in seconds, with citations. Replaces a tab of Redfin and a spreadsheet.

Amanda Foster

Luxury Broker · Boston, MA

"

The market-driven description actually reads like the neighborhood. Same house, two ZIPs, two different write-ups — and the one tuned to local buyers is the one that books showings.

Marcus Chen

Listing Agent · San Francisco, CA

"

Room classification with duplicate detection saves me from the worst part of the job. It picks the best shot of each space and the hero photo basically chooses itself.

Jennifer Rodriguez

Real Estate Photographer · Miami, FL

"

I used to spend an hour pulling comps and another hour writing the listing copy. Now I drop in 12 photos and an address, the agent gives me a UAD-graded report plus a description that sounds like me — I review and ship.

Sarah Mitchell

Real Estate Agent · Seattle, WA

"

The home report is the unlock. Sellers see a UAD grade with three ranked improvements and an ROI estimate — the conversation about painting cabinets or refinishing tile suddenly has numbers behind it.

Raj Patel

Brokerage Lead · Austin, TX

"

Being able to chat with the market data is the part my team didn't know they wanted. "What did this floor plan close at last year?" — answered in seconds, with citations. Replaces a tab of Redfin and a spreadsheet.

Amanda Foster

Luxury Broker · Boston, MA

"

The market-driven description actually reads like the neighborhood. Same house, two ZIPs, two different write-ups — and the one tuned to local buyers is the one that books showings.

Marcus Chen

Listing Agent · San Francisco, CA

"

Room classification with duplicate detection saves me from the worst part of the job. It picks the best shot of each space and the hero photo basically chooses itself.

Jennifer Rodriguez

Real Estate Photographer · Miami, FL

Frequently asked.

Four artifacts per run: (1) a live market dashboard for the property’s location — pricing, comps, demographics, walkability — with an AI assistant on top for explanations and Q&A, (2) a Fannie Mae UAD-calibrated home report that grades every room on quality (Q) and condition (C), (3) clean room-by-room classification of all your photos with duplicate detection, and (4) a market-driven listing description tuned to the local buyer archetype.

Photos and an address. That’s it. The address can be a full street address, a ZIP code, or a neighborhood — you’ll get more precise market intel with a full address. Beds, baths, square footage and listing price are optional; supplying them sharpens the description and the ROI numbers in the report.

Every photo is classified by room type, duplicate angles of the same room are collapsed, and each unique room is scored on Fannie Mae’s UAD Quality and Condition rating scales (Q1–Q6, C1–C6). The report rolls those up to an overall property grade and ranks improvement suggestions by estimated ROI in the local submarket.

It sits on top of the market dashboard, so anything visible there is fair game: median $/sqft and DOM, who’s buying on this block, recent closed comps, price tier distribution, year-over-year trends, walkability, school ratings, and how the subject property compares. It’s especially useful for explanations — "why is this number what it is?" — with citations back to the underlying comp set.

The description is conditioned on the local buyer archetype, the closed comps within a tight radius, the strongest features detected in your photos, and the property’s UAD grade. Same house in two different ZIPs gets two different descriptions — because two different buyer pools are reading it.

Greater Boston is in production today, with rolling expansion to the top 50 U.S. metros through 2026. Room types in scope: living rooms, bedrooms, kitchens, dining rooms, bathrooms, plus exterior and outdoor spaces.

Yes. The full pipeline — photo upload, room classification, home report, market analysis, listing description — is exposed as a REST API. Talk to us for endpoint docs and rate limits.

Your next listing deserves data, not boilerplate.

Tell us a bit about your market and we'll book a 20-minute walkthrough — bring photos and an address, we'll run a live home report.

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