My career has been inspired by challenger brands: companies with ambitions bigger than their budgets, set on winning customers from a market leader with more money, more stores and a better-known name. Most of them started with a founder who decided to make a better product: a non-stick pan good looking enough to sit on a stovetop, a button-down shirt soft enough to wear all weekend, an Oeko-tex certified playmat a new parent can put in a nursery.
The people behind these brands wear dozens of hats. They test hundreds (or thousands) of samples to choose a material, pack the first orders themselves, read every review, and answer customer emails late at night. Many put their own savings into the first production run, and the customers who find these products tend to come back. We admire these founders, and much of our work at Fabric is for them.
That grit and passion turned many of them into the leaders of their categories. Even so, the best product and the hardest work have never been a guarantee. Will customers find me? Where do I advertise, and will that spend pay back before cash runs low? What happens when a larger competitor bids more for the same shoppers?
In the past, shoppers might have preferred challenger products, for a new or important purchase 60% of shoppers preferred a brand (or retailer) they already know.
Need a new rug? Pottery Barn.
Need a dress shirt? Faherty.
A waterproof mascara? Lancome.
How AI changes the way shoppers discover products
A shopper's relationship with an AI is deeper than their relationship with a search box. AI engines have memory, recalling kids' ages and shoe sizes, cataloging preferences, understanding budgets and trips they are planning. A personal agent is even more broadly capable, linked to the shopper's calendar, email and payment accounts.
Rather than dictating a product, shoppers ask AI for help with a problem. "What do I need to setup my home office?" "What gear for the first camping trip with Boy Scouts?" To answer, the AI is able to interpret and define product requirements itself. AI can then gather the essential product information from across a broad internet, specifying use cases, fit, materials, dimensions, certifications.
Last week, Patrick Collison, CEO of Stripe, predicted that personal agents will act as "a kind of structural subsidy for product quality." What happens "if individuals always spent at least 10 hours researching their purchases"? With unlimited time and reach for research, shoppers may make smarter, and often different, product and purchasing decisions.
Where do challenger brands start? On Shopify
Many of the world's most loved brands run on Shopify, including Rhode, Bombas, Mejuri and Brooklinen, which Rich and Vicki Fulop launched with a Kickstarter campaign in 2014. These brands were first launching or early challengers when they chose Shopify, and they stayed on it as they became leaders in their categories.
The reason they chose Shopify is simple: they can start small, add Shop Pay or extend from a broad app ecosystem, and grow on the same platform. Moreover, the post-purchase experience of Shopify is rivaled only by the supply chain opportunities with Amazon; which is to say it is simple, fast, delightful.
In the past six weeks, Shopify has made those storefronts more accessible to personal agents, including Meta's Muse and Instinct, and opened checkout with Shop Pay. A company on Shopify can now be found and bought by an agent. But, whether it will choose to purchase depends entirely if the agent can understand what the brand sells.
Helping Shopify brands sell their products to AI
Commerce storefronts are designed for human shoppers to easily navigate and skim, compare products, and create an emotional connection. But, AI consumes information differently. It is nearly limitless, exacting and tireless, unconstrained by attention or time. Its strength? Consuming data voraciously, specifically the data that helps it fit a product to a shopper's problem.
So how do you feed AI the data it needs without overwhelming the human shopping experience? This is not a problem of storefront design and does not require changes to underlying commerce systems; it is a problem of data completeness and AI accessibility.
Fabric does this work for retailers and brands, helping them update their existing data and surfaces for AI shoppers while maintaining a best-in-class experience for people.
Introducing Fabric for Shopify
Today we announced that every Shopify brand can enrich its product data with Fabric in a few clicks.
Merchandising teams choose the products to enrich and review the results with guidance from Fabric. Fabric then publishes the enriched attributes, titles, descriptions, FAQs and image alt text back to Shopify: to the product detail page, to the product details AI engines read on the page, and to the core catalog data. All of it stays aligned to brand guidelines, voice, tone, and preferences. Meanwhile, teams can test, iterate, and optimize.
We built this because we agree with Patrick Collison: AI can give challenger brands an equal chance, and personal agents will help the best and best-suited products be discovered, considered and purchased. But, that only happens if the product information is there for AI to read.
Why we built this
Whether a brand is challenging a market leader or dominating the market, the products and the information about them will inform AI and what it recommends. Check out our official product news where we show how the Shopify integration works step by step and how merchandising teams use it. To learn more, visit fabric.inc.
