Cross-Border Product Selection and Profit Calculation Tool

Problem

Cross-border product selection often breaks down in two places: a product may look promising, but once platform commissions, FBA fees, inbound freight, ads, and returns are added, the margin is thin; and there are too many keywords and categories to manually compare for heat, competition, seasonality, and risk. This skill turns selection into a calculable workflow: estimate platform-level profit first, then use AI to analyze a keyword or image, and finally compare platforms before listing.

How it works

Profit calculation covers platforms such as Amazon US/UK/DE/JP, eBay, AliExpress, TikTok Shop, and Shopify. It models product cost, inbound freight, platform commission, FBA fulfillment, ad budget, returns loss, and storage fees, with air, sea, and rail freight options, then outputs margin, the best platform, and break-even order volume.
AI product analysis takes a keyword or category and evaluates market heat, competition, seasonality, margin space, and risks, then returns a recommendation score; it can use models such as Gemini, OpenAI GPT, or Kimi.
Image-based product discovery lets you upload a product image, lets a vision model infer product type, features, and material, and recommends similar hot products by target market and price range, reducing the gap from “seeing a product” to “finding comparable market signals.”
Cross-platform comparison uses the same product cost and weight to compare profit across platforms, which is useful for a quick pre-listing filter.

Limits

AI analysis generally requires an API key, and demo mode is available when a key is not configured. Platform fees, FBA costs, and logistics rates are reference values, so final decisions should follow the latest platform policies and live exchange rates. It is best used for cross-border sellers doing early product screening, profit simulation, and platform comparison, not as a formal accounting or inventory planning system.