Platform for marketplace sellers

The platform is a seller’s workspace on a large marketplace. It reads the platform’s data from six sources and writes changes back: prices, discounts, ad bids, replies to reviews, decisions on returns. The project stayed at the design stage: the research, the logic and the overall architecture are done - a map of 18 modules with a description of each.

What is inside

The modules fall into five blocks.

• Analytics. A daily pulse for every item, an anomaly detector, P&L, cash flow, unit economics, sales plan, ABC analysis.
• Pricing. Bulk price and discount changes, a unit economics calculator, FBO, FBS and DBS comparison, a repricer with rules and a margin floor.
• Promotion. Bid automation, search cluster performance, SEO keyword sets, position and shelf monitoring, A/B tests of the main photo on a live listing.
• Content and customers. AI-generated photos and infographics, reviews and auto-replies, questions and chats, return requests.
• Service. Supplies and stock, team tasks, notifications and roles, an AI chat over the brand’s data.

How the modules connect

The modules feed each other. Cost data from finance sets the floor for the repricer, search clusters suggest words for listing SEO and bids for the bid manager, and stock affects supplies and prices. The item row shows what changed and when, so the seller understands why sales went up or down.

Bulk changes first appear as a preview, then are confirmed, pass through limits and are logged. If a repricer rule goes wrong, it is visible and can be rolled back.

AI in the working screens

AI sits where the seller works every day: draft replies to reviews, hints on why sales dropped, demand and supply forecasts, copy for search clusters and a morning summary of the shop.

Who it fits

Marketplace sellers and teams with dozens or hundreds of items, agencies running several brands, and companies that want their own tool instead of subscribing to someone else’s analytics.