Ecommerce & consumer brands
The platform takes a fee, a dozen apps take a subscription each, and none of them share a customer record. The loyalty program can't see the order, the review request can't see the loyalty tier, and nobody can tell you what any of it actually earned. We replace the rented layer with software you own, on one record, and make the store readable and buyable by the AI assistants people are starting to shop through.
Who: Brands that sell their own catalog: apparel and footwear, home and outdoor goods, food and beverage, anyone with a deep product range, a wholesale side, and a platform bill they didn't choose.
Start with an AI Growth Audit of the storefront and the app stack.
The owned store
Stop paying the platform fee, the dozen apps, and the cut of every order. We build you a store you own outright, shaped to how you actually sell, and move your customers, orders and gift-card balances over with nothing lost.
For:brands whose platform bill grew faster than the brand did
In-stock sizes
A shopper filters to their size and sees only what's actually in stock. Fit advice comes from your own returns and reviews, not a generic chart. And when the size they wanted comes back, they're the first to know.
For:apparel, footwear, and anyone whose product comes in a matrix
Assistant checkout
When a customer asks ChatGPT or another assistant to find and buy something you sell, the assistant has to read your catalog, check stock and place the order. Most stores can't handle that yet. We build yours so it can.
For:brands getting ready for customers who shop through AI assistants
Review autopilot
Every finished job asks for its review at the moment your customer is happiest, and replies get drafted for you to approve. A year from now you own the map while competitors' pages go stale.
For:any business people look up before calling
A brand that sells online typically pays three ways: the platform's monthly fee, a stack of apps that each solve one problem the platform can't, and a percentage of every order on top. Each app keeps its own copy of the customer, so the same shopper is five different records in five different tools, and the questions that decide the year, what actually earned money and what actually cost it, get answered by exporting spreadsheets and guessing.
Ownership changes the economics and the answers at the same time. Catalog, cart, checkout, accounts and reviews run as your own code in one system, so every module sees the same customer and the same order history, and the app subscriptions stop. When you want to know what a product really returns after discounts, returns and app fees, you ask, because for the first time one system actually knows.
A deep catalog fails in one specific place: the moment a shopper looks for their own size. Most storefronts filter on what a product page claims, not on what inventory actually holds, so people click into items that are gone in the one dimension that decides the sale. Modeling size, width, length and fit as real structured data, and counting only what's actually buyable, is unglamorous work that pays for itself on the first busy weekend.
The same structure feeds everything downstream: the storefront's filters, the marketplace listings, the ad feeds, the size guidance built from your own return history, and the back-in-stock notes that know exactly which variant someone wanted. One catalog, modeled once, instead of four exports drifting apart.
Shopping is moving inside assistants. When someone asks an AI to find a product in their size and under their budget, the assistant has to read a catalog, check stock and complete a purchase, and it can only do that on stores built to be read. Most stores can't do that today. The brands that can are the ones that get recommended, and bought from, in a channel with almost no competition in it yet.
If a platform currently provides that layer for you, it's worth knowing it disappears the day you leave. We build it as yours: the catalog feed, the stock answers and the purchase flow, running on your own infrastructure whatever else changes around it.
Plenty of consumer brands quietly carry a wholesale side: teams, organizations and companies that order in bulk or with customization, served by an email thread and a spreadsheet because the store was never built for them. That is usually the fastest revenue on the roadmap, because the demand already exists and the flow just doesn't.
A real quote-to-order flow gives those buyers a price, an approval and a reorder button, lands every order on the same record as the rest of the business, and turns the largest orders you take from the least tracked into the best.
Ask your business anything
Questions
No. Plenty of this work runs alongside whatever you sell on today: the catalog modeling, the size-aware filtering, the owned reviews, the wholesale flow. When a move does make sense, we plan it as its own project with a rehearsal and a dual run, and you pick the cutover date. Nothing gets improvised on a live store.
It means an assistant can do three things on your store without a human clicking: read the catalog with real prices and options, check that a specific variant is in stock, and complete the purchase. We build and maintain those pieces as part of the store you own, so being recommended by the machines doesn't depend on a platform's roadmap.
That is most of what a careful migration is. Every URL is mapped and redirected, the product and review markup carries over, and rankings and traffic are watched through the cutover with a rollback ready. The move runs as a rehearsal first, then for real, on a date you choose outside your busy season.
The rent stops. The store is yours.
Built in Grand Rapids, Michigan, and put to work wherever your business is.