Build a Custom Dynamic Pricing Engine

Most stores start with off-the-shelf repricing software. These tools are effective at simple rule-based adjustments, like 'stay $0.01 below Amazon'. The failure mode is that they are black boxes; they cannot incorporate your store's unique data, such as warehouse inventory levels, shipping costs, or demand signals from a recent marketing campaign. They also charge escalating per-SKU or revenue-share fees.

A purely manual process using spreadsheets is worse. A merchandiser exports product data, manually scrapes a few competitor sites, and tries to calculate optimal prices in Google Sheets. This workflow is slow, does not scale beyond a few dozen products, and is immediately outdated. By the time the prices are updated in Shopify, the market has already moved.

We saw this with a PC components store using a popular SaaS repricer. A competitor ran a flash sale, and their tool correctly matched the low price on a popular graphics card. But it didn't know that inventory was low. They sold out in two hours at a minimal margin, missing the chance to sell the remaining stock at a premium after the competitor's sale ended. The SaaS tool lacked access to real-time inventory velocity.

Syntora's approach to implementing dynamic pricing begins with a comprehensive discovery phase to understand your specific business objectives, existing data infrastructure, and operational constraints. We would then design and build a custom system focused on data integrity, model transparency, and operational reliability.

The first step involves establishing a unified and resilient data pipeline. We would integrate directly with your ecommerce platform's API, such as Shopify or BigCommerce, to ingest product, order, and inventory data. Concurrently, a custom web scraper, developed using Python's Scrapy library and integrated with rotating proxies like Bright Data, would be deployed to collect real-time competitor prices and stock status. All raw data would be cleaned, transformed, and persistently stored in a Supabase Postgres database, creating a reliable foundation for all subsequent modeling. We have extensive experience building similar high-volume data ingestion and processing pipelines for various structured and unstructured data, including financial documents, and apply those robust engineering principles here.

With at least 12 months of historical sales and operational data, Syntora would develop the core pricing model. This model, often leveraging techniques like XGBoost, would be engineered to forecast demand at various price points, incorporating variables such as competitor pricing, inventory levels, seasonality, and potentially even real-time web analytics data. The model would be optimized to achieve your primary business goal, whether that is maximizing revenue or gross margin, while strictly adhering to defined constraints like floor prices and Minimum Advertised Price (MAP) policies, which would be managed through a Pydantic settings model for clear configuration.

The developed model and data pipelines would be integrated into a performant FastAPI application. This application would typically be deployed as a containerized service on AWS Fargate, providing scalability and resilience. For automated execution, a CloudWatch Events rule would be configured to trigger the pricing run at your desired frequency, for example, every 15 minutes. The system would be designed for efficient processing and timely updates to your ecommerce platform's API, ensuring prices reflect market dynamics quickly.

Robust monitoring would be integrated into the system from its inception. Syntora would implement structured logging using structlog, forwarding logs to AWS CloudWatch for centralized analysis and alerting. Critical alarms would be configured to trigger immediate notifications, such as via Slack, in the event of anomalies like a blocked scraper, data pipeline failures, or model suggestions exceeding predefined thresholds for price changes. The entire cloud infrastructure would be designed for cost-effectiveness and operational stability.

A typical engagement for a system of this complexity, managing thousands of SKUs, involves a build timeline of 10-16 weeks. Key client contributions include providing access to relevant APIs, defining precise business rules and constraints, and assisting with data validation. Deliverables would include the deployed, production-ready dynamic pricing system, comprehensive documentation, and knowledge transfer to your team for ongoing maintenance and future enhancements.