Retail: Dynamic Pricing Engine
Static pricing models resulting in lost margins during high demand and excess inventory during lulls.
The Challenge: The Speed of Commerce
In the hyper-competitive world of online retail, prices change by the minute. Our client, a general merchandise retailer, was updating prices once daily based on manual spreadsheet analysis. This latency meant they were often underpriced when demand was high (leaving money on the table) or overpriced when competitors discounted (losing sales).
They needed a system that could ingest millions of data points—competitor prices, stock levels, search trends, and historical sales—and adjust pricing in near real-time to maximize profitability.
Key Pain Points
- Manual pricing updates took 24 hours
- Inability to react to competitor flash sales
- Excess inventory carrying costs
- Lost revenue from stockouts on popular items
The Solution: Real-Time Dynamic Pricing
We engineered a high-frequency pricing engine using reinforcement learning to optimize for long-term profit rather than just short-term revenue.
Market Signal Ingestion
Scrapers and APIs collecting real-time pricing data from 50+ competitor sites.
Demand Forecasting
Time-series models predicting demand elasticity for 100,000+ SKUs.
Optimization Solver
Linear programming and RL agents determining the optimal price point for every product.
Price Sync Service
High-throughput pipeline pushing updates to the storefront and marketplaces instantly.
Implementation Roadmap
Disclaimer: This is a hypothetical use case with the purpose of just illustrating the potential for using AI for enhancing business tasks, processes and workflows.
Tech Stack
"The engine paid for itself in three months. It finds pockets of margin we didn't even know existed and protects us when the market gets aggressive."