Build a Custom Recommendation Engine For Your E-commerce Store
Building a recommendation engine involves analyzing purchase history to find product correlations. A custom model then uses this data to predict what a specific shopper will buy next.
Syntora designs and engineers custom recommendation engines for ecommerce platforms. Their approach focuses on auditing existing data, building robust data pipelines with Polars, and deploying collaborative filtering models via FastAPI on AWS Lambda to provide tailored product suggestions. They emphasize a comprehensive engineering engagement to deliver high-performance, maintainable systems.
The system's complexity depends on your data sources and business rules. A store with 18 months of clean Shopify order data offers a more direct path. A store needing to factor in inventory levels from a separate ERP and exclude out-of-stock items requires more intricate logic and a deeper discovery phase.
Syntora specializes in designing and engineering these bespoke AI systems. We approach each project as a partnership, starting with a comprehensive understanding of your unique data and business objectives to build a solution tailored precisely to your needs. To explore how we could build a recommendation engine for your store, book a call at cal.com/syntora/discover.
What Problem Does This Solve?
Most ecommerce stores start with a plugin from the Shopify App Store. These tools are easy to install but offer generic recommendations. They typically show 'trending products' or 'top sellers' to every visitor, which is not true personalization and does little to increase basket size. They cannot handle store-specific business rules, like not recommending a case for a phone already in the cart.
A common scenario is an online coffee retailer using a popular app. The app constantly recommended their main bestseller to every visitor, ignoring their purchase history. It showed a first-time buyer the same items as a three-year loyal customer. The app's pricing was 2.5% of attributed sales, which grew to a $1,200 monthly bill while average order value remained flat because the recommendations were not effective.
These plugins treat all stores the same. They cannot learn the unique purchasing patterns of your customers, like how buyers of one product are 7x more likely to buy another specific product within 30 days. This limitation means you leave money on the table with every transaction.
How Would Syntora Approach This?
Syntora's engagement would typically begin with a discovery phase, auditing your existing ecommerce platform APIs and data sources, such as Shopify or BigCommerce, to understand available order history. We would then design a data pipeline to extract and transform the last 18-24 months of relevant transaction data.
For data processing, we would leverage Polars to create a user-item interaction matrix, mapping customer purchases to products. This robust approach is designed to efficiently handle large datasets, supporting millions of order line items. We would then train a collaborative filtering model using the Python library LightFM. This library is well-suited for sparse ecommerce data, where individual customers typically interact with only a small fraction of your total product catalog. The model learns latent features to predict items a user might be interested in, even if they haven't viewed them before.
The trained model would be deployed as a serverless function using FastAPI on AWS Lambda. When a shopper views a product, your website would securely send the customer ID to this API endpoint. The Lambda function would then generate a set of tailored recommendations, returning the product SKUs to your storefront. This architecture prioritizes fast response times to ensure recommendations load directly without impacting page performance.
To maintain freshness, every relevant user interaction would be logged to a Supabase database. A scheduled job would retrain the model regularly, such as weekly, using the latest sales data to adapt to new trends and product launches. We would also configure CloudWatch alerts to monitor the API for any issues with latency or error rates, ensuring the system remains highly available and performs as expected.
A typical build of this complexity would range from 6 to 10 weeks, depending on data cleanliness and integration requirements. Clients would need to provide API access to their ecommerce platform and participate in defining key business rules and integration points. Deliverables would include the deployed recommendation engine API, detailed documentation, and knowledge transfer for ongoing maintenance. We have extensive experience building similar high-performance data pipelines and API services for complex document processing in financial services using components like FastAPI and Claude API, and apply that same rigorous engineering discipline to new domains.
What Are the Key Benefits?
Live in 4 Weeks, Not 4 Months
From data audit to a live production API in 20 business days. Your store gets smarter recommendations quickly without a long implementation project.
Pay for the Build, Not Your Revenue
This is a one-time project with a low, fixed monthly hosting fee. We do not charge a percentage of the sales the system generates for you.
You Own The Source Code
You receive the complete Python codebase and the trained model in your private GitHub repository. It is your asset to modify or extend in the future.
Weekly Retraining For Freshness
The system automatically retrains on new sales data every Sunday morning. Your recommendations adapt to new products and changing customer tastes.
Integrates With Your Tech Stack
The API returns raw product data, which can be styled in any Shopify theme, used in Klaviyo email campaigns, or integrated into a checkout flow.
What Does the Process Look Like?
Week 1: Data Audit & Strategy
You grant read-only API access to your ecommerce platform. We analyze your order history and product catalog and deliver a project plan outlining the model strategy.
Week 2: Model Development
We build and train the recommendation model on your data. You receive a performance report showing how accurately it predicts purchases on a test dataset.
Week 3: API Deployment
We deploy the finalized model to a secure API endpoint. We provide your developer with API documentation and a key for front-end integration.
Week 4: Integration & Handoff
We support your developer during integration and monitor the API's performance for 30 days. You receive the full source code and a system runbook.
Frequently Asked Questions
- How much does a custom recommendation engine cost?
- A typical build is a 4-6 week engagement. The final price depends on your order volume, SKU count, and any custom business logic required. For a store with clean data and under 1,000 products, it is a standard project. We provide a fixed-price quote after a discovery call where we can assess your specific requirements.
- What happens if the recommendation API goes down?
- The API is deployed on AWS Lambda for high availability. In the rare event of an outage, your website's front-end code should be configured to gracefully fall back to showing a default list of best-selling products. This prevents a broken user experience. We use CloudWatch for monitoring and are alerted immediately to restore service.
- How is this different from a Shopify App Store plugin?
- App store plugins are multi-tenant SaaS tools offering one-size-fits-all widgets. We build a single-tenant system trained exclusively on your data, which you own completely. This allows for far more accurate personalization and the inclusion of your specific business rules. There are no revenue-sharing fees, only a predictable one-time build cost.
- How do you handle recommendations for new customers or new products?
- This is the 'cold start' problem. For new customers with no purchase history, the system defaults to showing your store's most popular or highest-rated items. For new products, we use a content-based approach, recommending them based on similar product tags and descriptions until they accumulate enough purchase data for the main model.
- Do I need an in-house developer to work with Syntora?
- Yes. We deliver a production-ready API, but you will need a front-end developer to integrate the recommendations into your store's theme. We provide clear API documentation and support your developer throughout the integration process to ensure a smooth launch, but we do not perform front-end theme development ourselves.
- What is the minimum amount of data we need to build a good model?
- For a high-performing collaborative filtering model, we recommend at least 12 months of sales history and a minimum of 5,000 total orders. For stores with less data, we can build a simpler 'frequently bought together' model as a starting point. We verify your data is sufficient during the initial audit before any build work begins.
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