AI Recommendation Engine for E-commerce
Every e-commerce visitor sees the same products. An AI recommendation engine shows each shopper what they'll actually buy.
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. Treating them identically means your homepage, product pages, and search results all carry dead weight. 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.
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
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
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The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
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?
Plain answer
RaftLabs builds custom AI recommendation engines for e-commerce: collaborative filtering on purchase history, content-based recommendations from product attributes, personalised homepages, upsell and cross-sell widgets, and search personalisation using embeddings. We integrate with Shopify, WooCommerce, and custom stacks, with a dashboard tracking click-through, add-to-cart, and attributed revenue per placement. A validated v1 launches in 10 to 14 weeks at a fixed cost.
What to remember
- Approximate nearest-neighbour search and precomputed product embeddings mean personalised ranking across 100,000+ SKUs returns in milliseconds regardless of catalogue size.
- A hybrid model blends content-based signals for low-purchase-count items with collaborative signals for high-purchase-count items, solving the cold-start problem for long-tail products.
- Embedding-based query understanding handles typos, synonyms, and multi-word intent so search results don't depend on exact keyword matches.
- A build covering collaborative filtering, personalised homepage, and upsell widgets runs $25,000-$60,000; content-based recommendations and search personalisation bring it to $60,000-$120,000.
Two shoppers, one homepage, the same twelve products.
A first-time visitor and a customer who has bought from you eleven times land on the same homepage and see the identical twelve products. One of them has told you, through every order and every browse, exactly what she's likely to want next. The page ignores all of it.
Every product-page upsell is hand-picked, the same for everyone, and it went stale months ago. The store already holds the signals to do better. Nothing is reading them.
The right product, for the right shopper, at the right point in the journey. That's the whole job.
The goal isn't novelty for its own sake. It's a 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.
RaftLabs has shipped production software since 2015 for clients across the US, UK, Europe, Canada, and the UAE. We build recommendation engines on Shopify, WooCommerce, and custom stacks. We scope the work and lock a fixed price before development starts, and the team that assesses your data and catalogue is the team that ships the engine. No offshore handoff after signing. Precomputed embeddings and approximate nearest-neighbour search keep personalised ranking fast across large catalogues, and an A/B testing framework is live from day one so the revenue lift is measured, not assumed. The collaborative filtering engine and core placements launch as a validated v1 in 10 to 14 weeks, then grow from there.
Recommendations already carry a large share of demand at the retailers that do this well. The numbers below are why the right engine pays back.
- of Amazon purchases come from its recommendation engine
- 35%
- McKinsey, 2013
- of Netflix viewing is driven by recommendations
- 75%
- McKinsey, 2013
This works when your store already has signal to learn from.
Everything on the left should already be true for your store. Even one thing on the right, and a manual best-sellers row or an off-the-shelf widget is the smarter first step.
An online store with 6-12 months of order history and a few thousand customers, enough signal for collaborative filtering to work.
You run Shopify, WooCommerce, or a custom stack and want to personalise homepages, product pages, upsell widgets, or search.
Every visitor currently sees the same manually curated products, and you have budget for a build from $25,000.
A brand-new store with little order history or only a handful of customers.
A catalogue small enough that a hand-picked best-sellers row already covers it.
You want a plug-in widget you can install this afternoon, not a system built on your own data.
Scope
What we build
- 01
Collaborative filtering recommendations
Matrix factorisation builds a personalised affinity score per customer, updated in real time, with segment-level fallback for cold-start users and A/B testing infrastructure to promote the winning variant. - 02
Content-based recommendations
Semantic embeddings from product descriptions and attributes let similar items surface even without exact word overlap, indexed in Pinecone or pgvector for sub-10ms similarity lookups across catalogues with hundreds of thousands of SKUs. - 03
Personalised homepage and category pages
Each visitor sees a product arrangement built from their own history, with session-aware recency weighting boosting items browsed in the last 20 minutes. - 04
Upsell and cross-sell widgets
"Frequently bought together" and checkout cross-sell widgets are driven by real purchase co-occurrence data, with post-purchase email sequences recommending the next logical product. - 05
Search personalisation
Results rerank per user by purchase history, with embedding-based query understanding handling typos and synonyms, and zero-results queries redirected to the nearest relevant category. - 06
Recommendation analytics
A dashboard tracks click-through, add-to-cart rate, and attributed revenue per placement, with statistically validated A/B test results and segment analysis by customer type.
Have an AI recommendation project?
Tell us your platform, your catalogue size, and which placements you want to personalise. We'll scope the right engine and give you a fixed cost.
How it works
From scope to live recommendation engine
- Week 101
Data and catalogue scoping
We map your platform, catalogue size, and available order/browse history. You leave week 1 with a written scope document and a fixed-price quote.
- Weeks 2-402
Model and placement design
Collaborative and content-based model selection, plus placement strategy, designed against your actual traffic and data volume.
- Weeks 5-1103
Build and integrate
Recommendation engine, embeddings pipeline, and analytics dashboard built in parallel, tested against real catalogue data.
- Final 2-3 weeks04
Launch and A/B measurement
A/B testing framework live from day one, with a clean control group to measure revenue lift.
What you pay depends on scope, not negotiation:
- Focused build, $25,000-$60,000
- Collaborative filtering, a personalised homepage, and upsell/cross-sell widgets, with a Shopify integration.
- Full build, $60,000-$120,000
- Everything in the focused build, plus content-based recommendations, search personalisation, and a full analytics dashboard.
What it costs
Custom recommendation engine, starting at $25,000.
A scoped engine on your data and platform, with the placements, embeddings pipeline, and analytics dashboard it needs to move revenue.
Start with collaborative filtering on your highest-traffic placements. Once the A/B test shows real lift, we scope search personalisation and the rest of the engine.
Starting investment
Starts at $25,000
The collaborative filtering engine and core placements ship first, live in 10 to 14 weeks with A/B testing included. Add content-based recommendations and search personalisation once the lift is proven.
No hourly billing
We scope the engine and lock that price in writing before any development starts. No hourly billing, no surprise invoices. A scope change is a priced change request, agreed before work begins.
Measured lift
The A/B testing framework is live from day one with a clean control group, so you measure real revenue lift against a control, not an assumption.
Work with us
Tell us where the work is stuck.
Bring the rough workflow, half-built product, or messy brief. We will map the smallest useful first move, then send scope, timeline, and price in plain English.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
Common questions
Collaborative filtering and content-based models both scale well. We use approximate nearest-neighbour search so personalised ranking across 100,000+ SKUs returns in milliseconds, with precomputed and indexed embeddings for constant lookup time. For long-tail products with few purchases, we use a hybrid model blending content-based and collaborative signals.
The core inputs are order history, product catalogue data, and browse events. For Shopify stores, we pull order and catalogue data from the Admin API and instrument browse events via a JavaScript tag. Most stores need 6-12 months of data and a few thousand customers for meaningful collaborative filtering.
Engagement metrics like click-through rate are visible from day one. A statistically significant revenue lift is typically measurable within 4 to 6 weeks of launch, depending on traffic volume. We set up an A/B testing framework as part of the build so you have a clean control group from launch.
A recommendation engine covering collaborative filtering, personalised homepage, and upsell/cross-sell widgets with a Shopify integration typically runs $25,000 to $60,000. Adding content-based recommendations, search personalisation, and a full analytics dashboard typically runs $60,000 to $120,000.
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