Computer vision for listing photos

Every listing photo,
turned into structured data.

Stryder reads a property's photo set the way a trained appraiser would, room by room, and returns structured tags, condition notes, and virtual tour assets your brokerage can act on in minutes, not days.

FIG. 01 · ANALYSIS OUTPUT Sample Stryder analysis output A line-drawing floor plan with four annotated callouts: room type with a confidence score, a condition score with confidence, a detected feature, and a flooring detail. ROOM TYPE KITCHEN · 0.94 CONF. CONDITION 8.1/10 · 0.91 CONF. FEATURE NATURAL LIGHT FLOORING ENGINEERED WOOD

The problem

Photo sets pile up faster than anyone can review them.

A busy brokerage brings in hundreds of new listing photo sets a week. Someone still has to look at every image, decide what room it is, judge its condition, note what's renovated and what isn't, and get it all into the MLS listing and marketing package correctly. That review work doesn't scale with headcount, and inconsistency between agents shows up in the listings themselves.

The approach

Stryder reads the photos first, so your team reviews instead of transcribes.

Upload a listing's photo set and Stryder classifies each room, scores condition, detects notable features, and assembles the images into an ordered virtual tour with a draft floor plan layout. Your team checks the output and pushes it forward, instead of starting from a blank listing form.

01 · INGEST

Drop in the photo set

Connect your existing upload flow or drag in a folder. Stryder accepts raw photo sets straight from the listing shoot, in whatever order they arrive.

02 · ANALYZE

Room-by-room read

Each image is classified by room type, scored for condition, and scanned for features worth calling out: renovated kitchen, natural light, exposed damage, and similar.

03 · STRUCTURE & DELIVER

Ready for the listing

Output arrives as structured tags plus an ordered virtual tour and a draft floor plan, ready to drop into your MLS entry or marketing package.

Under the hood

Every output ships with the confidence to act on it

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Confidence-scored, not just labeled

Every room classification and condition score carries a confidence rating, so your team knows when to trust the first pass and when to take a closer look.

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Scored by area, not just overall

Kitchen, bathroom, interior, and exterior are scored separately before rolling up into one property score, so nothing gets averaged away.

Find comparable properties

Search across your own listing history for visually similar units, useful for pulling comps or matching a buyer to properties they haven't seen yet.

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Damage flagged by severity and system

Flags aren't just noted. Each one is rated by severity and mapped to the building system it affects: roofing, plumbing, electrical, and so on.

Platform scale

Built to keep pace with a brokerage's actual photo volume.

Figures below are illustrative targets for the current build, shown to demonstrate the kind of scale reporting the platform is designed to produce, not live customer data. See the Validation page for how these are measured.

12,400+
Property photo sets processed
96%
Room-type classification accuracy on holdout set
1.8M
Individual images analyzed to date
<4min
Average turnaround per listing photo set

Technology in use

A Saturday listing surge, handled the same way every time

A brokerage's photographer finishes a shoot Saturday morning and uploads 42 raw images straight from the site. By the time the listing agent sits down Monday, Stryder has already done the first pass.

Every image is tagged by room, scored for condition, and checked for the features that actually move a listing. The agent's job becomes reviewing and approving, not sorting through a memory card room by room. For the full walkthrough of how this pipeline works, see Technology.

  • Room classification across kitchens, bedrooms, bathrooms, living spaces, and exteriors
  • Condition scoring on a consistent 1–10 scale, broken down by area with a confidence rating on every score
  • Feature detection for renovation, natural light, outdoor space, and visible damage
  • Damage flags rated by severity and mapped to the building system they affect
  • Comparable-property search across your own listing history for comps and buyer matching
  • Auto-ordered virtual tour and a draft floor plan layout from the photo set alone
ANALYSIS_OUTPUT.json 42 IMAGES
room_typekitchen · 0.94 conf.
condition_score8.4 / 10 · 0.91 conf.
sub_scoreskitchen 8.6 · bath 7.9 · ext 8.0
renovation_detectedtrue: countertop, cabinetry
natural_lighthigh
visible_damagemoderate · plumbing, hallway
comparable_matches6 similar units in portfolio
tour_sequenceauto-ordered, 42/42
floor_plan_draftgenerated
kitchen primary bedroom renovated bath backyard natural light flag: hallway ceiling

For brokerages

Consistency across every agent, without adding headcount

Faster time-to-list

First-pass review that used to take an afternoon is ready before the agent opens their laptop.

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Consistent data quality

Every listing gets the same condition scale and feature checklist, regardless of which agent shot it.

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Fits your existing stack

Structured output is built to drop into your MLS entry and marketing workflow, not replace it.

Scale without more reviewers

Volume weeks stop meaning backlog. The first pass keeps up with the photographer's schedule.

See it read a photo set from one of your own listings

Bring a real listing's photo set to the walkthrough and we'll run it live, so you can judge the output against a property you already know.