Our way
Compositive
Elm Street · Bethesda, MD · $1,350,000
- Input
- Your photos · MLS data · public APIs
- Output
- Your property, composed frame by frame
- Hallucinations
- ZERO — every pixel traces to a source
Now in early access
Cinematic listing videos. Your photographs. No number without a name.
Real estate listing video that doesn’t fabricate.
We compose the property, the data, the neighborhood — frame by frame, from sources you can name. Watch a full sample listing in two and a half minutes.
First wave locks founder pricingNo card required
See the difference
Raw listing photos in. A cinematic, data-rich video out.
Drag the slider. The same sample listing — raw listing photos on one side, the composed video with real neighborhood data on the other.
Exterior
Great Room
Kitchen
Pool & Spa6-stage render pipeline
See what happens when you paste a URL.
One listing URL triggers a six-stage pipeline: depth maps, room classification, live data from public sources, scene composition, neural narration, and a cinematic render. Scroll to watch each stage — or tap a step to jump.
Photo analysisBuilding depth maps
8 photos detected · per-room depth estimation
2
Room classification
3
Data enrichment
4
Scene composition
5
Audio
6
Render
Building depth maps
Stage 1 of 6
DEPTH ✓DEPTH ✓DEPTH ✓DEPTH ✓Live preview — illustrative of a real processing run.
Six stages. About 90 seconds. Every time.
The moat
We orchestrate reality. They guess at it.
There are three ways to make a video of this home. The generative path: write a prompt, accept a plausible house the model invents. The slideshow path: crossfade your photos, no story. The compositive path — ours: bring real photos and real data, and the engine composes them into cinema, frame-accurate and source-cited.
The two other ways everyone settles for
Legacy
Slideshow
- Input
- Your photos only
- Output
- Crossfades, no neighborhood story
- Hallucinations
- None — but also no data
Generative AI
Generative
- Input
- A text prompt
- Output
- A new home the model invented
- Hallucinations
- High — inherent to the method
Determinism is the moat.Same input renders the same video — every render, every time.
Generative AI vs a real listing video — the full breakdown
Hiring a videographer vs a DIY listing video — cost and speed compared
The data layer, made visible
Real photos in the frame. Real data around it.
Every number you see was pulled live from a public source and composed into the cut — NCES school data, FEMA hazard ratings, EPA air quality, mortgage math, and OpenStreetMap places. Not invented. Sourced.
Composed from a real listing. No templates, no editing, no stock footage — paste a URL or drop your photos and the engine does the rest. This loop is muted — hear the narration in the full demo.
Get this on your next listing.
Join the first wave to lock founder pricing — your first listing video is $9, watermark-free, and you see it before you pay.
Built by Blue Snow Developers — an independent studio with multiple shipped, live products.
Or watch the full cut, compare the tiers & pricing, read how we handle your data, or meet the team building it.
Every figure, its receipt
Everything you just watched came from a real data source.
We don’t generate video from prompts. We compose it from data you can audit. Here is each data scene at full size — exactly as it renders — beside the number it shows, the source it came from, and the math you can re-run. If the underlying data changes, the next render reflects it. That’s the contract.
6
Nearest public schools
Bethesda Elementary (K–5) · 645 students · 16.5:1 · 48% math, 52% reading · Bethesda-Chevy Chase High (9–12) · 2377 students · 16.7:1 · 71% math, 87% reading · Somerset Elementary (K–5) · 314 students · 15:1 · 43% math, 55% reading
Enrolment, grade span and pupil-teacher ratio come from the NCES Common Core of Data; the proficiency percentages are the state's own EDFacts assessment for 2020-21. We publish their fields — we do not score schools.
Source: NCES CCD · U.S. Dept. of Education (EDFacts)On screen: “Nearest 4 of 137 MD public schools within 10 miles · NCES CCD 2024”
24.3 mi
Sidewalk within 1 km
24.3 mi sidewalk · 2.5 mi bike path · 4.3 mi trails, measured inside a 1 km radius
Every footway, cycleway and path in OpenStreetMap inside the radius, summed by haversine over the published node geometry. It is mapped ground, not an index — you can open the same map and re-measure it.
Source: OpenStreetMap (ODbL)On screen: “Getting Around”
70
Air quality index — Moderate
AQI 70 Moderate · main pollutant PM2.5 · observed at the nearest reporting area, with the observation time on screen
EPA's AirNow index for the nearest reporting area. The reading is stamped with its own observation time, because an air-quality number without a timestamp is a different claim than the one the EPA made.
Source: EPA AirNowOn screen: “Air Quality · EPA’s air quality index”
2 min
To the nearest grocery
Lidl 2 min · Bethesda station 3 min · Baltimore/Washington Intl Thurgood Marshall 49 min
Driving times are routed over the OpenStreetMap road graph, and highway distances are measured to the named interchange. No traffic model is claimed — it is the route, not a prediction of your Tuesday.
Source: OpenStreetMap · OSRM routingOn screen: “Travel Times · Highway Access”
$6,948/mo
Estimated monthly payment
6.67% 30-yr fixed · $270K down (20%) · principal and interest only, and the scene says so
The 30-year fixed rate is Freddie Mac’s weekly survey as published on FRED; the payment is standard amortisation on the list price — arithmetic you can re-run, not an estimate we invented.
Source: Freddie Mac via FRED (Federal Reserve)On screen: “Monthly Cost · Monthly, sourced”
Very Low
Expected annual loss — all hazards
Montgomery County, MD census tract · flood zone X, not in a FEMA Special Flood Hazard Area · FEMA National Risk Index · December 2025
FEMA’s National Risk Index, reported for the census tract and labelled as the tract’s figure — never the house’s. We use expected annual loss, not the composite risk rating, because the composite folds in social vulnerability.
Source: FEMA National Risk IndexOn screen: “Natural Hazard Risk · FEMA maps this location”
31
Places mapped nearby
10 categories — Restaurants, Grocery, Cafes, Bakeries, Parks, Recreation, Fitness, Culture, Shopping, Civic · 0.1–4.9 mi
Every place is an OpenStreetMap record and every distance is measured from the property’s own coordinates. The scene states its span rather than calling it "walking distance".
Source: OpenStreetMapOn screen: “Daily Life Here · 0.1–4.9 mi”
One render. Public data, credited on screen. Zero prompts.
What is in the film
Fifteen beats, and 10 of them carry published data.
Every beat below renders from your photographs or from data published about your address. A data beat is left out when its source cannot answer for that address — we would rather show you fourteen scenes than invent a fifteenth.
Watch the engine
One engine. Every tier.
Each template in the catalog audits a different power of the engine — luxury restraint, starter-home approachability, condo-tower modernism. Same deterministic pipeline. Same real-data contract. Pick one in the catalog and apply it to your next listing in one click.
The full cut
A sample listing in Bethesda, Maryland. Real photographs, NCES school data, FEMA hazard ratings, real mortgage math, EPA air quality, and OpenStreetMap places and sidewalks. Composed end-to-end by the engine — same render every time.
More tiers, more templates, one engine.
Every tier includes the full template range — starter homes, condos, townhomes, multi-family and the luxury tier you just watched. See what each plan includes and pick the look that matches your brand.
The comparison
The only listing video with the neighborhood in it.
Four ways agents make listing videos in 2026 — and the one that composes real data, share pages, and lead capture into a single render.
Scroll right to compare →
| Feature | TargetVid | Template editors | Generative AI | AI avatar tools | Videographer |
|---|---|---|---|---|---|
| Real neighborhood data scenesSchools, flood risk, air quality, walkability, mortgage, commute — animated in-video | |||||
| Live public-API data layerNCES, FEMA, EPA, FRED, OpenStreetMap, OpenMapTiles — each figure credited to its publisher | Every figure credited | ||||
| Paste a listing URL → videoAutomated photo extraction, data enrichment, scene composition | |||||
| Your own photographs · nothing generatedCompositive rendering — no AI-invented rooms, furniture, or geometry | |||||
| Interactive share page + lead captureBranded landing page per video, viewer analytics, lead forms | |||||
| Synthesized narration + captionsNeural narration with word-level captions and music ducking | Manual | ||||
| Cinematic depth parallaxDepth-mapped parallax gives still photographs real camera motion | Filmed | ||||
| Deterministic — same render every timeFrame-accurate, reproducible output; re-render reflects updated data | |||||
| Time to finished video | < 5 min | 20–30 min | 15–60 min | ~10 min | 3–7 days |
| Typical price | From $9 / video | ~$15 / mo | ~$19–49 / mo | ~$24–89 / mo | $150–600 / shoot |
Generative AI
Invented rooms & warped geometry
Fully AI-generated imagery fabricates fixtures and spaces that aren’t in the real home — a buyer-trust and disclosure risk.
AI avatar tools
A talking head, not the home
Avatar-first tools center a presenter, with no neighborhood data layer and no per-listing share funnel.
Hiring a videographer
Days of turnaround, hundreds a shoot
Beautiful footage, but slow and costly per listing — and still no animated data scenes.
A comparison of the common approaches to making a listing video. Typical times and prices reflect general market rates as of May 2026, not any one product.
Who it’s for
Built for the people who close listings.
Solo agent
More inquiries on every listing.
Listing video is the asset most agents skip — too much time, too much edit cost. We turn the listing you already have into the video you didn’t have time to make, in about 90 seconds, from photos you already shot.
Built for the agent who shoots their own photos and posts their own listings.
Brokerage owner
Listing video that scales across your whole roster.
Most listings still go to market without video. The agents who do post video keep showing up first in the feed. We give your brokerage the production pipeline that scales — same template, every agent, every listing.
One template, every agent. Designed for brokerage rollout in 2026 H2.
Photography vendor
Add video to your package. Without adding video work.
You’re already at the property with photos. We turn those photos into a cinematic listing video — same trip, additional line item, zero added shoot time.
Add-on pricing $99-$249 typical. Adjust per market.
The platform
One engine. Many verticals.
We built the compositive engine for real estate because that’s where the data is richest and the inquiry math is clearest. The engine doesn’t know it’s a real-estate engine. Any vertical with structured data composes on the same rails — one week of templates, not one year of engineering.
Real estate
LiveListing videos with schools, hazards, air quality and walkability.
Mortgage
Q3 2026Rate-environment + monthly-cost breakdowns for loan officers.
Memorial
In designObituary tribute videos composed from photos + life dates.
Auto dealers
In designNew-inventory cinematic walkarounds from photos + spec data.
Brokerage
LiveMulti-agent team licensing on the same render pipeline.
Custom
On requestYour vertical, on the same engine. Tell us what data you have.
Don’t see your vertical? Tell us. The engine ports in a week. The waitlist remembers.
FAQ
Questions we hear most.
Last updated: 2026-05-14.
First wave
The first wave opens soon. We’ll save you a seat.
Real-estate agents go first. Mortgage, auto, and memorial follow. Drop your email — we send one note the day your wave opens.
First video $9, then pay per video — no subscription. No card required for early access.
Explore TargetVid
Pricing — pay per video, founder rateJoin the early-access waitlistWatch the full demoAbout the teamContact usHelp centerTrust centerChangelog & recent updatesSystem statusPrivacy policyTerms of serviceDMCA notice & takedownWhat you get with early accessThe engine behind every renderGenerative AI vs a real listing videoHiring a videographer vs DIY AI videoGetting started guideSecurity & compliance