Python · Pandas · APIs
Cross-Marketplace Repricing Engine
Combines Amazon and Walmart price feeds with your own cost data and rules to propose or push new prices, with guardrails on minimum margin.
Screens are illustrative layouts of the product, not client screenshots.
Overview
What it is
A repricing engine for marketplace sellers. It reads competitor prices from Amazon and Walmart, applies your cost data and strategy rules, and proposes or pushes new prices, never below your margin floor.
The problem
Why it was needed
Manual repricing couldn't keep up with competitor changes, and simple tools either undercut blindly or ignored costs, eroding margin.
Features
What We Built
Strategy rules
Match, undercut, hold or follow a target Buy Box.
Margin guardrails
Cost, fees and minimum margin per SKU.
Dry-run mode
See the effect of rules before pushing prices.
Feed submission
Push approved prices back to the marketplaces.
Change log
Who or what changed each price, and why.
Dashboard
Prices, margins and rule hit-rates.
Architecture
How It Fits Together
- Competitor feedsAmazon and Walmart data
- Cost dataSKU costs and fees
- Rule enginepandas + strategies
- Approval / dry runreview before push
- Feed submissionmarketplace APIs
- Change logaudit trail
Rules decide, guardrails limit, and everything is logged, so repricing stays explainable.
Roadmap
From Idea to Launch
A typical delivery roadmap for a project of this kind.
- 1
Strategy workshop
3-5 days- Define repricing strategies per product group
- Margin and fee model
- Edge cases: stock-outs, no competitors
- 2
Data layer
1-2 weeks- Competitor feed ingestion
- Cost and fee data
- SKU matching across marketplaces
- 3
Rule engine
2 weeks- Strategy implementation
- Guardrails
- Dry-run mode
- 4
Push and audit
1 week- Feed submission
- Change log
- Approval workflow
- 5
Launch
1 week- Pilot on a product subset
- Dashboards
- Gradual rollout
Want something like this?
Tell us what it should do and we'll come back with a stack, a scope and a timeline.