Your homepage shows the same products to every visitor regardless of their purchase or browse history?
Upsell widgets on product pages are manually curated, go stale, and are the same for every customer regardless of what they've bought before?
Search returns irrelevant results for multi-word or synonym queries because it relies on exact keyword matching?
AI Recommendation Engine for E-commerce
Custom AI recommendation engines for e-commerce -- collaborative filtering, personalised homepages, upsell and cross-sell widgets, and search personalisation built to increase average order value and repeat purchase rate.
We've shipped 100+ products and understand how recommendation placement, algorithm choice, and cold-start handling affect revenue. Off-the-shelf recommendation plugins use the same logic for every store. A custom engine is calibrated to your catalogue, your customer behaviour, and the placements that move your metrics.
Collaborative filtering on purchase history with real-time personalisation and cold-start handling for new users
Content-based recommendations from product attributes and semantic embeddings -- works for new items with no purchase history
Personalised homepage and category pages with session-aware recency weighting
Recommendation analytics tracking click-through rate, add-to-cart rate, and revenue attributed per placement
RaftLabs builds AI recommendation engines for e-commerce brands -- collaborative filtering on purchase history, content-based recommendations from product attributes, personalised homepages and category pages, upsell and cross-sell widgets on product and checkout pages, and search personalisation using embeddings. We integrate with Shopify, WooCommerce, and custom stacks, and give you a recommendation analytics dashboard tracking click-through rate, add-to-cart rate, and attributed revenue per placement. Most recommendation engine projects deliver in 10--14 weeks at a fixed cost.
100+Products shipped
·Real-timePersonalisation
·FixedCost delivery
·10-14Week delivery
Showing every visitor the same products is leaving revenue on the table.
Every customer who lands on your store has a different history -- what they've bought, what they've browsed, what price range they shop in, which brands they prefer. Treating them identically means your homepage, product pages, and search results are all carrying dead weight: products that are irrelevant to that person at that moment. A recommendation engine fixes that by making every placement specific to the visitor in front of it.
We build recommendation systems that use the actual signals in your store data -- purchase history, browse behaviour, product attributes, and search queries -- to surface the right product to the right customer at the right point in the buying journey. The goal isn't novelty for its own sake. It's higher click-through rate on product listings, more items added to cart per session, and a checkout value that reflects what each customer actually wanted to buy.
What we build
Collaborative filtering recommendations
Matrix factorisation on your purchase history data to build the "customers who bought X also bought Y" signal at scale. Every customer gets a personalised affinity score across your catalogue, updated in real time as they browse and buy. Cold-start handling for new users who have no purchase history -- we fall back to segment-level recommendations based on session signals like entry page, referral source, and device type until enough individual signal accumulates to personalise. A/B testing infrastructure lets you run multiple recommendation algorithms against each other and promote the variant that lifts revenue.
Content-based recommendations
Recommendations built from product attributes -- category, price range, brand, material, size range, and any custom fields in your catalogue. We generate semantic embeddings from product descriptions and attribute sets so that "waterproof hiking boots" is understood to be similar to "trail running shoes with gore-tex" even if the exact words don't overlap. This approach works for new products with no purchase history and for niche catalogues where collaborative filtering lacks enough signal density. It also handles catalogue changes cleanly -- new products are embedded and become recommendable the day they're published.
Personalised homepage and category pages
Each visitor sees a homepage product arrangement built from their browse and purchase history rather than a static editorial selection. A returning customer who buys outdoor gear sees outdoor products ranked to the top of every grid. A first-time visitor from an outdoor brand ad sees a version calibrated to that acquisition context. Category pages rerank within-category results per user so that the products most likely to convert for that customer appear first. Session-aware recency weighting means a product browsed in the last 20 minutes gets a boost -- the engine responds to what the customer is actually looking at right now.
Upsell and cross-sell widgets
Product detail page widgets -- "complete the look", "frequently bought together", and "upgrade to" -- driven by real purchase co-occurrence data rather than manual curation. Checkout page cross-sell for impulse add-ons: low-cost complementary items shown at the point of highest purchase intent. Post-purchase email cross-sell sequences that recommend the next logical product based on what the customer just bought -- a customer who bought a coffee grinder gets a recommendation for coffee beans and a cleaning brush, not another grinder. Every widget placement is independently tracked so you know which recommendation type is generating revenue and which is not.
Search personalisation
Search results ranked differently per user based on their purchase and browse history. A customer who consistently buys premium brands sees premium results ranked to the top of the same query that shows mid-range results to a price-sensitive customer. Embedding-based query understanding handles typos, synonyms, and multi-word intent: "waterproof jacket women's" returns the right results even if your catalogue uses "women's waterproof rain jacket" in product titles. Facet suggestion based on user segment -- a customer who always filters by a specific brand gets that filter surfaced as a quick-select. Zero-results queries are caught and redirected to the nearest relevant category rather than dead-ending the session.
Recommendation analytics
A dashboard showing click-through rate per recommendation placement, add-to-cart rate from recommendation clicks, and revenue directly attributed to recommendations across all placements combined and broken down individually. A/B test results with statistical significance indicators so you know when a variant has generated enough data to act on. Segment analysis showing which recommendation types perform best for which user groups -- new versus returning, high-value versus occasional buyers, mobile versus desktop. The data that tells you where recommendation is working, where it is dragging, and where to invest next in the system.
Frequently asked questions
Collaborative filtering and content-based models both scale well to large catalogues. For collaborative filtering, we use approximate nearest-neighbour search so that personalised ranking across 100,000+ SKUs returns in milliseconds rather than seconds. For content-based models, product embeddings are precomputed and indexed, so lookup time is constant regardless of catalogue size. The main challenge with very large catalogues is the cold-start problem for long-tail products with few purchases -- we address that with a hybrid model that blends content-based signals for low-purchase-count items with collaborative signals for high-purchase-count items. We size the infrastructure to your catalogue and traffic volume during scoping.
The core inputs are order history (customer ID, product IDs, order date, order value), product catalogue data (attributes, categories, prices, descriptions), and browse events (page views, product views, add-to-cart events). For Shopify stores, we pull order history and catalogue data from the Shopify Admin API and instrument browse events via a lightweight JavaScript tag on the storefront. For custom stacks, we integrate with your existing data pipeline or instrument events directly. Most stores have enough order history to build a meaningful collaborative filtering model with 6--12 months of data and a few thousand customers. We'll tell you during scoping if your data volume requires a different approach.
You'll see engagement metrics -- click-through rate and add-to-cart rate from recommendation placements -- from the day the engine goes live. Revenue attribution takes longer to measure cleanly because you need enough sessions with and without the engine to run a valid comparison. For most stores, a statistically significant revenue lift signal is visible within 4--6 weeks of launch. The most important factor is traffic volume -- higher-traffic stores produce statistically significant results faster. We set up an A/B testing framework as part of the build so you have a clean control group to measure against from day one.
Yes. For Shopify, we integrate via the Storefront API for frontend personalisation and the Admin API for order and catalogue data. Recommendation widgets embed in your existing theme using Shopify app blocks or direct theme component injection. For Shopify Plus stores, we can personalise checkout recommendations using checkout extensibility. For custom stacks, we expose the recommendation engine as an API that your frontend queries per page load, and we provide a JavaScript SDK for browse event tracking. The integration approach depends on your stack and deployment setup -- we cover this during the scoping call.
Talk to us about your AI recommendation project.
Tell us your platform, your catalogue size, and what recommendation placements you want to personalise. We'll scope the right engine and give you a fixed cost.
