Image and video decision
AISelfi.es
A competent technical user can reproduce the core experience using existing open-source UIs and local inference stacks; the product's value mainly comes from polish, curated styles, and brand rather than unreproducible data or proprietary models.
Visit website↗Built by Jon Kraayenbrink, who ships 7 products in this index
Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off40 h to build
$120/mo6 h/mo upkeep
No published price to break even against.
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
- User uploads selfies → system trains a personalized model / tuner on those images → user selects style/outfit/background → system generates a batch of high-resolution photos → user downloads images and optionally requests refund.
What it still won’t have
- Proprietary trained models and model improvements from the vendor
- Brand, polished UX and marketing reach
- The vendor's refunds and customer support workflow
- Pre-curated catalogue of 100+ ready-made styles and their curated prompts
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
AISelfi.es 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
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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 self-hosted 'selfie-to-professional-photo' service using: React frontend, Node.js (Express) API, PostgreSQL for metadata, MinIO or S3 for object storage, and a dockerized inference stack based on AUTOMATIC1111 + diffusers for per-user model personalization; include: (1) secure user upload UI with server-side validation, (2) job queue (Redis + Bull) to run dataset prep and per-user tuning (or textual inversion / DreamBooth) using the chosen open-source tooling, (3) generation endpoint producing batches of 4K images with style presets, (4) simple payment/order flow (Stripe) for one-time payments, (5) gallery/download page, (6) privacy/deletion workflow that purges user images and models after 30 days, (7) logging, error handling, and unit + integration tests for API endpoints. Out of scope: training large base models from scratch, multi-tenant enterprise billing, and mobile-native apps.
How we checked
How the score was reached
- Build verdict base78
- 4 cited sources+3
- Evidence score81
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 · 4
Every page the run actually retrieved.
- official productAISelfi.es — official product page
- official productAISelfi.es — pricing and packs
- official productAISelfi.es — how it works
- official productAISelfi.es — privacy & retention
Integrity checks
What held up, and what did not.
