AI Recommendation Engine for E-commerce

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. 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

Recent outcomes

Voice AI · Research

6× deeper insights

Text-based interviews converted to automated phone calls

AI Automation · Ops

20k+ txns day one

Manual invoice OCR across 40+ gas stations

Loyalty · Retail

1,062 users in 4 weeks

SuperValu & Centra loyalty platform with receipt validation

SaaS · Logistics

2,000+ shipments yr 1

Multi-carrier shipping hub for Indonesian eCommerce

4.9
on Clutch
See our work

The problem

Sound familiar?

  • 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?

Short answer

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 to 14 weeks at a fixed cost.

Key takeaways

  • 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.

Trusted by

Vodafone logo
Aldi logo
Nike logo
Microsoft logo
Heineken logo
Cisco logo
Calorgas logo
Energia Rewards logo

Recommendation engine delivery, by the numbers

products shipped
100+
personalisation
Real-time
cost delivery
Fixed
week delivery
10-14

Showing every visitor the same products is leaving revenue on the table

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.

Capabilities

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 for sub-10ms similarity lookups across catalogues with hundreds of thousands of SKUs.

    Built with
    Pinecone · pgvector
  • 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.

How we work

From scope to live recommendation engine

  1. Week 1
    01

    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.

  2. Weeks 2-4
    02

    Model and placement design

    Collaborative and content-based model selection, plus placement strategy, designed against your actual traffic and data volume.

  3. Weeks 5-11
    03

    Build and integrate

    Recommendation engine, embeddings pipeline, and analytics dashboard built in parallel, tested against real catalogue data.

  4. Final 2-3 weeks
    04

    Launch and A/B measurement

    A/B testing framework live from day one, with a clean control group to measure revenue lift.

Why us

Why e-commerce brands choose RaftLabs

  • 01
    Senior engineers build what they scope

    The engineers who assess your data and catalogue also build the solution. No bait-and-switch, no offshore handoff after the contract is signed.

  • 02
    Fixed price before development starts

    We scope the work, calculate the cost, and lock it in writing before any development starts.

  • 03
    9 years and 100+ products shipped

    Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record building ML-driven e-commerce platforms.

  • 04
    Milliseconds, not seconds, at scale

    Precomputed embeddings and ANN search keep personalised ranking fast across massive catalogues.

  • 05
    Measured, not assumed, revenue lift

    A/B testing is built in from day one, not bolted on after launch.

Have an 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.

Talk about your AI recommendation project

Related services

AI Recommendation Engine for E-commerce, scoped in one call.

Tell us what's broken. Within one business day you get a straight take on cost, timeline, and the right first step. No deck, no pressure.

Stay on topic

More on retail & ecommerce

Custom Retail Inventory Management Software

Read more
AI in Logistics: Cutting Costs on Thin Margins

AI in Logistics: Cutting Costs on Thin Margins

Logistics runs on margins that leave no room for inefficiency. Carriers, fuel, dwell time, customs delays, every friction point is a cost that compounds across millions of shipments. AI addresses each of these, but not equally. Here is where the numbers actually justify the investment.

Read more
Digital Commerce Software Development

Digital Commerce Software Development

Headless storefronts, subscription engines, marketplace platforms, and DTC infrastructure for brands that need more than off-the-shelf.

Read more

Frequently asked 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.

Work with us

Tell us what you need. We'll tell you what it would take.

We scope AI Recommendation Engine for E-commerce in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.

  • 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.

Go deeper

Browse our case studies

Digital commerce software by area