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Koderead

Transformation Gallery

Seven systems you can open.

Each one starts from a real operational problem, follows a mapped workflow, and ends in a console your team would actually work in.

Built by Koderead to show the operating system. Figures are a worked example.

Direct-to-consumer retail

AI Growth Console

Our merchandising decisions are weekly. Our data is weekly. Our competitors are daily.

A direct-to-consumer retailer where margin, returns and stock risk are all knowable and none of them are visible until a Monday export. Abandoned baskets are chased with one template regardless of why they were abandoned, and the marketing team spends its week assembling reports rather than acting on them.

Open AI Growth Console

GRGrowth

MerchRetail

Abandonment, classified by reason

Basket-abandonment breakdown — AI Growth Console
ReasonBasketsRecoveredAction taken
Shipping cost at checkout2,140486Threshold reminder + free-shipping tier shown
Payment failure890401Retry link, alternative method offered
Size or fit hesitation1,630208Fit guidance + free returns surfaced
Comparison shopping1,18072No discount sent — margin protected
Out of stock variant57217Back-in-stock notification offered

1,184 recovered in total. Note the comparison-shopping row: the system deliberately sends no discount there, because that cohort converts anyway and a discount only gives away margin.

Flagged for a merchandiser

  • LM-4471 Linen Shirt

    Return rate 31% vs 9% category

  • LM-2210 Canvas Tote

    Margin 11.2%, below 18% floor

  • LM-8802 Wool Scarf

    14 days cover at current velocity

Asked in plain language

“Which SKUs lost us money last month?”

→ 3 SKUs below margin floor · query and source shown with the answer

Orders per month
24,800
Active SKUs
3,140
Marketing team
5
Reporting lag, before
6 days