Case study · AI Commerce

Touchable.ai

From a photo to a printed deck

For Touchable.ai, A Few Good Coders built the personalized commerce flow that takes a customer from photo input and AI generation through deck configuration, live preview and purchase, shipped as a live web storefront for custom tarot, playing and conversation decks.

AI-personalized commerce flow
photo → print
Client
Touchable
Industry
AI Commerce
Public listings
Website
Evidence
Checked September 2026

The challenge

A custom physical product needs to make AI generation, configuration, preview and purchase feel like one coherent flow. Customers start with a photo and end with a printed deck delivered to their door. In between, several systems have to cooperate: image generation, a configurator, a preview that matches what will be printed, and checkout.

If any step feels disconnected, the customer loses confidence that the deck they receive will be the deck they designed.

What we were responsible for

We were responsible for the personalized commerce flow from end to end: photo input, AI generation, deck configuration, live preview and purchase. That flow is the product. It is where a customer decides whether to buy.

The evidence for this work is the live product website. We do not publish sales figures for Touchable.ai.

How we approached it

The live preview sits at the centre of the experience. It has to show the customer an honest representation of the physical product, so that what they approve on screen is what arrives. That makes preview accuracy a commerce problem, not only a rendering one.

AI generation adds variability. The flow has to handle generated results gracefully, let customers configure their deck around them, and keep the path to purchase short once they are happy with what they see.

Print and delivery close the loop. A personalized product is only finished when it reaches the customer, so the digital flow has to hand over everything the physical process needs.

What shipped

A live web storefront for custom tarot, playing and conversation decks.

What this case shows

When AI output becomes a physical product, every step between generation and delivery is part of the customer experience. Treating that path as a single flow is what makes personalization feel trustworthy enough to pay for.

How the engagement runs

Every project, this one included, follows the same four beats. They keep the scope honest and the work visible.

  1. T-0

    Fit and risk

    We clarify the goal, the constraints, the existing system, and the decision that matters most. If we are not the right team, we say so.

  2. Week 1

    A bounded first milestone

    Agree on the outcome, what is excluded, who is working on it, and what evidence will count as done, before the scope grows.

  3. Every week

    Make the work visible

    Repository access, working demos, written decisions, and a visible change log. New scope becomes an explicit tradeoff before we proceed.

  4. Handover

    Leave the team ready to own it

    Code, accounts, documentation, and release knowledge stay with you. Ongoing support is optional and scoped before launch.

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