Analytics and monitoring decision
Rooftops AI
A technical user can build a one-week minimal roof-measure-and-estimate tool that covers the core instant-report workflow, but the full paid product—verified NWS storm history, photo-based shingle identification, and the trained AI assistant with agent features—would require additional data integrations and models that make a complete replacement impractical for a single developer.
Visit website↗$28/mo
$336/yr
Read off the official pricing page.
$100one-off38 h to build
$75/mo3 h/mo upkeep
On cash alone, building overtakes the subscription at 3 seats.
No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
What a replacement has to do
- Enter address → fetch satellite + elevation → segment roof facets → compute area/pitch → generate cost estimate → export proposal PDF
What it still won’t have
- NWS-verified hail history integrated into every report
- Shingle ID from a single rooftop photo with manufacturer/product identification
- Roofy AI assistant with domain-trained chat, live web search, and automated agent workflows
- Door-knock tracking, team seats/leaderboards, and the full AI Agent Suite (follow-ups/lead nurturing)
What remains hard
- Product polish and ongoing maintenance
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 3 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal Rooftops-like service using Python FastAPI, Postgres, and a simple React UI. Scope: (1) accept an address and geocode it; (2) fetch satellite tiles and elevation data for the location; (3) run an off-the-shelf roof-segmentation model to extract facet polygons; (4) compute per-facet area, aggregate squares with a configurable waste factor, and compute pitch from elevation+facet slope; (5) apply a rule-based cost estimator that outputs a low/median/high range and confidence; (6) render a branded proposal PDF and provide email export. Out of scope: shingle-photo identification, NWS hail history ingestion, conversational AI assistant, team/seat management. Include authentication, error handling, unit tests for geometry and estimator logic, and deployment scripts (Docker + single small VPS).
How we checked
How the score was reached
- Partly verdict base52
- 3 cited sources+3
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Evidence score62
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 3
Every page the run actually retrieved.
- official productRooftops.ai — product page
- official pricingRooftops.ai — pricing
- official productRooftops.ai — use cases
Integrity checks
What held up, and what did not.
