Automate Recommendations and Upsells with Custom AI

Most stores start with a Shopify app for recommendations. These tools often rely on simple co-occurrence logic, showing what other customers bought. This fails for new products with no purchase history and creates repetitive suggestions, pushing the same five bestsellers on every page.

A home goods store selling artisan ceramics found their app recommended their most popular mug to customers viewing a $400 vase. The logic was simplistic, missing the nuance of price point and product category. Worse, the app's JavaScript added 900ms to their product page load times, hurting conversion rates and SEO rankings.

Email platforms like Klaviyo offer their own recommendation blocks, but the logic is a black box. You cannot specify rules like 'If a customer buys a coffee machine, upsell them filters and cleaning kits, not another coffee machine'. These off-the-shelf tools cannot handle business logic and force your store into a one-size-fits-all model.

Syntora would approach developing a custom product recommendation system by first auditing your existing data sources, such as 18 months of Shopify order data and your complete product catalog information. Using Python and pandas, this data would be processed and cleaned to create a robust history of purchases from every customer. This curated dataset would then form the basis for developing a collaborative filtering model, designed to identify true product relationships based on user behavior.

To address the cold-start problem for new products, the architecture would integrate the Claude API to analyze your product descriptions and generate vector embeddings. This method allows for recommendations of new items based on textual similarity to products with an established purchase history. Syntora has experience building sophisticated document processing pipelines using the Claude API for financial documents, and the same pattern applies effectively for analyzing e-commerce product data. This hybrid model, combining user behavior and product content, would be stored in a Supabase Postgres database, optimized for fast lookups.

The core recommendation logic would be encapsulated within a FastAPI application and designed for deployment as a serverless function, typically on AWS Lambda. When a customer visits a product page, a request would query this API. The function would retrieve the user's history and relevant product features, compute a list of personalized recommendations, and return them efficiently. The hosting infrastructure for such a system would be designed for cost-effectiveness and scalability, with typical initial deployments often costing under $100 per month.

Syntora would integrate these recommendations directly into your Shopify theme's Liquid files using a lightweight JavaScript snippet configured to load asynchronously. This integration approach ensures minimal impact on your initial page load speed or Core Web Vitals. The same API endpoint could also be leveraged to feed personalized product blocks into your Klaviyo email templates, maintaining consistency across your on-site and email marketing channels. A typical engagement for a system of this complexity would range from 6 to 10 weeks, and would require the client to provide access to their Shopify and Klaviyo data, along with input on specific business rules. Deliverables would include the deployed recommendation service, integration code, and full documentation.