AI / Consumer · Case study

Cardify

AI-powered trading card platform with a custom multi-model image pipeline, nano banana powering the in-app editor, a 3D card-slab viewer, and a full creator marketplace. Built from a founder idea, no visual rough draft.

Starting from
Founder idea only · No visual rough draft · No prototype · No backend · No image-model decisions · No marketplace · No card editor
Shipped
September 2, 2025 · Last updated May 17, 2026
Cardify trading card platform — "Create Epic Trading Cards" hero over a grid of AI-generated card artwork, with Generate Card CTA, Buy Credits, Upload Designs, and Browse Marketplace tiles
001 / The numbers

What shipped, in figures

Cardify
0
Visual rough draft inherited
3
Image engines, tested and chosen per job
22+
Postgres tables (marketplace + creator economy)
2 / 2
Dual auth (email + wallet) and dual payments (Stripe + crypto)
002 / The arc

From founder vision to shipped product

Three steps · No filler
01

Where they were stuck

Cardify isn't slotting into an existing category — it's trying to create one from nothing. There's no incumbent to clone, no reference product to point at. The founders had a clear pitch — physical AI-generated trading cards with a card editor, 3D preview, marketplace, and creator-economy payouts — but no prototype, no visual mockup, no chosen image model, no backend schema, no editor, no 3D viewer, no commerce path, no fulfillment plan. Even the basic question of which image model could actually produce something printable as a trading card hadn't been answered. Every layer needed to be designed and built from a blank page, taking direction from the founders on product intent and shipping the engineering, design, and AI-model decisions ourselves.

02

What we built

The Notus designed and built the entire product from blank file. The image question was central, so we spent real time testing the current generation of image and image-editing models against the actual job of producing something printable at trading-card dimensions, and landed on a custom multi-model pipeline with one engine per job: one for generation, nano banana for the conversational in-app editor (with a pre-distortion step we wrote after testing surfaced an aspect-ratio bias), and a third for masked inpainting with a browser mask painter and a custom analyzer that translates painted regions into prompt language. Around that pipeline sit four card editors, a Three.js card-slab 3D viewer, a Supabase backend with 22+ tables, a full marketplace with creator-economy payouts, dual auth (email and Privy wallet), dual payments (Stripe and crypto), a custom LPIPS duplicate-detection service on Hugging Face Spaces, and an n8n workflow for Stripe → Easyship fulfillment.

03

What shipped

Cardify is live at cardify.club. Users sign in with email or a wallet, generate a card, refine it conversationally inside the nano-banana-powered editor or surgically with the inpaint mask painter, see it rendered as a real 3D card-in-slab object, list it on the marketplace, and either get paid out in credits or cash through Stripe. The credits ledger has never double-credited a Stripe payment because the duplicate-detection happens at the database insert, not in application code. The LPIPS service catches near-duplicate uploads before they pollute the marketplace and routes ambiguous cases to a human review queue. The fulfillment glue runs unattended every 12 hours. The product the founders described in the first call now exists end-to-end, designed and built from the blank file outward.

003 / What we built

The technical work, in plain terms

Production engineering · 10 systems
Stack
Production engineering delivered
Up next
Song Cage

A songwriting app that runs on web, iOS, Android, and inside DAWs — one React codebase, custom melody detection, offline word tools. Designed and built from a founder vision, no prototype to start from.

Get a free production audit →