Image and video decision
Cleopatra AI
A capable developer can build a local nose-editing prototype using existing open-source face-manipulation and GPU image libraries, but reproducing the polished App Store product (in-app purchase flows, localization, polish, and user acquisition) is larger work and not fully covered by the supplied prior art.
View on the App Store↗Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off120 h to build
$0/mo3 h/mo upkeep
No published price to break even against.
Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Cleopatra AI alternatives, with the arithmetic →
What a replacement has to do
- 1) Accept and validate a user selfie upload (camera + gallery) and normalize image. 2) Detect facial landmarks / crop and align face region (landmark model). 3) Run a local image-edit model that reshapes the nose region (on-device ML model inference and intensity slider). 4) Blend/retouch edited region back into full face with color/texture smoothing. 5) UI flows: before/after comparison, save/export, and adjustable intensity controls.
What it still won’t have
- App Store presence, reviews, and existing user base/ratings
- Polished UX, localization and accessibility work matching store app
- In-app purchase plumbing and payment handling
- Ongoing marketing, A/B tests and analytics configured by the vendor
- Legal/compliance work and published privacy policy pages
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
Cleopatra AI does not publish a price we could read, so there is nothing to compare against. What building costs is below.
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 iOS nose-editor app in Swift using SwiftUI and Combine, CoreML for on-device inference, and Metal/GPUImage for blending/retouch. Core features in scope: camera/photo picker, facial-landmark detection, crop/align face, run a converted nose-reshaping PyTorch model (provide PyTorch-to-CoreML or torchscript conversion) with an intensity slider, seamless blending and basic skin/texture smoothing, before/after compare UI, save/export to Photos, and local-only processing. Out of scope: App Store publishing, analytics instrumentation, in-app purchase server receipts, and surgeon consultation workflow. Include error handling for model load/failures, unit tests for image pipeline functions, and end-to-end UI tests for the main flow.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 5 cited sources+3
- Evidence score60
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 · 5
Every page the run actually retrieved.
- official productNose Editor & Face - Cleopatra on App Store
- official productNose Editor & Face - Cleopatra on App Store (pricing/in-app)
- official productNose Editor & Face - Cleopatra on App Store (privacy/local processing)
- open sourceCyberTimon/RapidRAW
- open sourcehacksider/Deep-Live-Cam
Integrity checks
What held up, and what did not.
