Build a Custom Dynamic Pricing Engine
Ecommerce stores use AI to set prices based on real-time competitor data, demand, and inventory levels. The system automates price adjustments to maximize profit margins or revenue without manual intervention.
Syntora helps ecommerce stores implement custom AI-driven dynamic pricing systems. We design comprehensive solutions that integrate real-time competitor data, demand forecasts, and inventory levels to automatically optimize product prices for maximum profit or revenue, built as a bespoke engineering engagement.
Understanding your current catalog size, the dynamism of competitor pricing, and your specific profit or revenue goals is the first step in scoping an effective dynamic pricing solution. A system designed for 500 SKUs with a simple 'match lowest price' rule requires a different architectural approach than one managing 50,000 SKUs that necessitates price elasticity modeling and MAP compliance.
Syntora approaches dynamic pricing as a bespoke engineering engagement, designing systems that integrate directly with your existing ecommerce platform and business logic. We focus on building robust, transparent solutions tailored to your unique operational context and growth objectives.
What Problem Does This Solve?
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 This workflow is slow, does not scale beyond a few dozen products, and is immediately outdated.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.
How Would Syntora Approach This?
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.
What Are the Key Benefits?
Price Updates Every 15 Minutes, Not Daily
React to competitor changes and demand spikes in near real-time. The system completes its pricing cycle in under 2 minutes, ensuring you never miss a margin opportunity.
Own the Asset, Ditch the Subscription
Pay a one-time build fee and minimal monthly hosting costs. Avoid per-SKU fees or revenue-sharing models from SaaS pricing tools that penalize growth.
You Get the Source Code and the Model
We deliver the full Python source code in your private GitHub repository. The pricing logic is yours to own and modify, not a black box controlled by a vendor.
Alerts Before Problems Cost You Money
We configure CloudWatch alarms for scraper failures or unusual price swings. Get a Slack alert if a competitor changes their HTML, before your data goes stale.
Direct API-to-API Integration
The system connects directly to Shopify, BigCommerce, or Magento APIs. No manual CSV uploads or fragile intermediate steps. Price changes are written directly to your catalog.
What Does the Process Look Like?
Strategy & Data Connection (Week 1)
You grant read-only API access to your ecommerce platform and analytics. We define the pricing strategy, constraints like MAP, and target competitors.
Model & Scraper Build (Weeks 2-3)
We build the competitor scrapers and the core pricing model. You receive a backtest report showing how the model would have performed on your last 6 months of sales data.
Deployment & Live Testing (Week 4)
We deploy the system to AWS and run it in a logging-only mode for several days to verify price suggestions. Once approved, we enable live price updates on a small product subset.
Monitoring & Handoff (Weeks 5-8)
We monitor the system for 4 weeks post-launch to ensure stability. You receive the complete source code, documentation, and a runbook for maintenance and monitoring.
Frequently Asked Questions
- What does a custom dynamic pricing system cost to build?
- The primary cost drivers are the number of competitors to scrape and the complexity of your pricing rules. A system for a store with 500 SKUs and two competitors is much simpler than one for 50,000 SKUs and ten competitors with complex MAP rules. We scope every project on a fixed-fee basis after a discovery call. Book a call at cal.com/syntora/discover to get a specific quote.
- What happens if a competitor's website changes and the scraper breaks?
- The system is designed to fail gracefully. It detects a drop in valid scraped data and sends an immediate Slack alert. It then holds the last known valid price for that competitor and continues to reprice based on your other data signals. We typically repair a broken scraper within one business day as part of our post-launch support.
- How is this different from using a SaaS tool like PriceLent or Wiser?
- SaaS tools offer configurable rules within a multi-tenant platform. Syntora builds a single-tenant system just for you. This allows us to incorporate your unique data sources, such as internal inventory forecasts or marketing calendars, which generic tools cannot. You also own the intellectual property, including the code and the trained model, outright.
- How much historical data is needed to start?
- We need at least six months of sales and inventory data, but 12 to 18 months is ideal. This history is crucial for accurately modeling seasonality and the relationship between price changes and sales volume (price elasticity). Stores with fewer than 100 SKUs or very sparse sales data may not be a good fit for this approach.
- How do we know the AI's prices are correct before going live?
- We never go live immediately. The system first runs in a 'dry run' mode for at least a week. It calculates and logs its suggested prices to a dashboard without pushing them to your store. This allows your team to review the suggestions against their own expertise and build confidence in the model's logic before activating it.
- Do we need an engineer on our team to manage this system?
- No. The system is designed for autonomous operation with built-in monitoring and alerting. We provide a runbook that a non-technical person can follow for common situations. Syntora handles all maintenance during the initial monitoring period and offers an ongoing support retainer for a flat monthly fee after the handoff is complete.
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