Only retail. Nothing else.
Every engineer on the team came from retail or retail data. Not fintech, not health, not general software consulting. That focus is why we ship a first production result in four weeks instead of four months.
Retail AI · Four buyer types
Retail AI, built by the team that shipped it inside Kohl's and Sears.
For D2C brands, ecommerce and marketplace sellers, wholesalers, and multi-store retailers doing $5M–$50M. First production result in four weeks. Ship-or-don't-bill.
Retail-only. Nothing else. 60+ combined years in retail and ecommerce data Built at Kohl's, Sears, and HappySellers (250+ live sellers) Ship-or-don't-bill. You own the code.
Which buyer type are you?
01 / 04
D2C Brands
Shopify, WooCommerce, BigCommerce, Magento.
Founder-led or founder-owned brands doing $5M–$50M, DTC first, often with a growing wholesale side. Strong catalogue, real seasonality, no data team.
The pain, in operator language
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Sales up, margin quietly shrinking, because true contribution per SKU is unknown.
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Bestseller stocks out in week two of the campaign while slow movers eat cash.
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Recommendation widget shipped with the theme, generic and unchanging.
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Returns rate creeping up with no per-SKU or per-cohort visibility.
What we ship for this buyer type
Sample outcomes
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Stockouts 8.1% → 2.4% in one apparel season.
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Average order value +18% inside the first cycle.
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31% less excess inventory held, no rise in stockouts.
02 / 04
Ecommerce & Marketplace Sellers
Amazon Seller Central, Walmart, TikTok Shop, plus a Shopify DTC store.
Marketplace-first operators doing $5M–$50M across Amazon and Walmart, often with a DTC storefront on the side. FBA and FBM mix, hundreds to thousands of ASINs.
The pain, in operator language
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Marketplace settlement files land as thousand-line CSVs that ops reconciles by hand.
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Chargebacks and deductions eaten because nobody has time to dispute inside the window.
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FBA storage costs climbing while the hero ASIN goes out of stock every peak.
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Buy-box win rate drifts down and nobody knows when to move on price and when to hold.
What we ship for this buyer type
Sample outcomes
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12 hours a week of ops time returned to the team.
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FBA storage fees down in the double digits per quarter.
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Buy-box win on core ASINs recovered inside a cycle.
03 / 04
Wholesale & Distribution
Brands selling into retailers and distributors, plus B2B pure-plays.
$10M–$50M wholesalers, distributors and DTC brands with a serious wholesale channel. Buyers include the majors, boutique chains, independents. NetSuite, Cin7, or ERP-backed.
The pain, in operator language
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Sell-through by door lives with the buyer, weeks late. Reorder timing is a guess.
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Manual PO-to-invoice-to-payment reconciliation across dozens of accounts.
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Wholesale demand planning done in a spreadsheet that breaks when a new account scales.
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Committed inventory (already sold to a buyer) not netted correctly against Shopify stock — leading to overselling online.
What we ship for this buyer type
Sample outcomes
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Weekly SKU × door velocity from buyer files that were previously unreadable.
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AR close time down 60–70%.
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One inventory number that means one thing across DTC and wholesale.
04 / 04
Multi-store Retail
Chains with 10+ physical locations, POS + ecommerce.
Retail chains with 10+ doors, POS + ecommerce, 500+ active SKUs and category managers who own the buy. Small central IT, no dedicated data team.
The pain, in operator language
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Store-level reorder decisions made from a POS report that runs weekly and lags the shelf.
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Category assortment planning done on a whiteboard because no model handles store × category.
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Merchandising rules that were written three seasons ago, still ranking product pages.
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Loyalty data untouched — no segmentation, no repeat-purchase signal, no LTV bands.
What we ship for this buyer type
Sample outcomes
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Store-level reorder recommendations by SKU × door, refreshed daily.
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Category assortment planning that respects store profile and local demand.
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Behavioural segmentation on the loyalty file, feeding retention campaigns.
Why hire a specialist team.
A full-service AI agency re-learns retail on your budget. A specialist team does not.
Built by the team that built it at Kohl's
Recommendation engines for 20M+ shoppers. Demand forecasting through multiple Black Friday cycles at Sears. Now shipped at the size of a $5M–$50M business.
Tested on our own GMV first
HappySellers is the retail platform we own — 250+ live sellers, 6,000+ registered businesses. Every technique we recommend has run on real orders before it touches yours.
Ship-or-don't-bill
Every milestone has a specific number to hit on a specific date. If it does not ship, you do not pay. Month-to-month after milestone one — no lock-in.
Common questions
Frequently asked
What is retail AI, in plain English?
Machine learning applied to retail decisions — predict demand, price on purpose, reconcile settlements overnight, tell you what to reorder. Trained on your own transaction, inventory and settlement data. Runs inside your stack. You own the models and the code.
Which of the four buyer types is closest to us?
D2C brands (Shopify-first, direct), Ecommerce & marketplace sellers (Amazon/Walmart primary), Wholesale & distribution (selling into retailers), and Multi-store retail (10+ physical doors). Most of our clients sit inside one of these — a handful sit across two. We'll tell you which one you actually are on the fit call.
Which ecommerce platforms do you work with?
Shopify, Shopify Plus, WooCommerce, Magento, BigCommerce on the storefront side. Amazon Seller Central (FBA and FBM), Walmart, TikTok Shop on the marketplace side. NetSuite, Cin7, Brightpearl, or custom ERP for wholesale. POS: Shopify POS, Square, Lightspeed and the big chain systems. If you're on something else, ask on the call — we've integrated with most stacks in the $5M–$50M range.
What is the first AI use case most retail operators should tackle?
For D2C and multi-store, demand forecasting — the ROI is measurable inside one season. For marketplace sellers and wholesalers, reconciliation automation — fastest payback because it returns operator time immediately. For anyone with a large catalogue and repeat-purchase behaviour, a recommendation engine typically returns fastest on top-line revenue. The right answer depends on your margin profile, data quality and where you're currently losing the most money. We rank it on the first call.
How is this different from a SaaS forecasting or reconciliation app?
SaaS tools ship a generic model and ask you to fit into it. Custom retail AI is trained on your data, respects your channel mix, and ships as code you own. For most $5M–$50M operators, the difference is 20–40% better forecast accuracy and the ability to model things a generic app cannot see — like inventory committed to a wholesale PO that Shopify does not know about.
How much data do we need to start?
Demand forecasting: typically 12–24 months of order history and current stock levels. Recommendations: transaction history and product catalogue. Returns: order-level return records with 6+ months of history. Reconciliation: raw settlement files from the marketplaces. We assess data readiness on the fit call and tell you exactly what's needed before anything is signed.
What does an engagement cost?
AI Fit Sprint from $5K, delivers a prioritised roadmap. Implementation retainers $8K–$20K per month depending on scope and system count. Milestone-based, with published ranges. No mystery quotes.
How is a wholesale + DTC brand different from a pure DTC brand?
Wholesale + DTC is where most Shopify-native tools quietly break. Shopify does not know 3,000 units of a SKU are already committed to a wholesale PO shipping in March. So the forecast is confidently wrong, ops maintains a parallel spreadsheet, and nobody has a single true number. We build the data layer that spans both channels — that's usually the first project.
Ready to talk?
Book a 30-minute call.
We'll rank the highest-ROI use case for your business right now. If it's not a fit, we'll say so on the call.