Skip to main content
Download free report
SoftBlues
E-commerce & Retail • AI Product Development

AI-Powered Product Recommendation System for E-commerce

Smart E-commerce Recommendation Engine

An AI recommendation engine that delivers real-time personalized product suggestions by analyzing customer behavior, product relationships, and context to boost engagement and sales.

Book a Case Walkthrough
0%
Better Relevance
0%
More Discovery
0%
Cross-Sell Boost
0%
Recommendation Accuracy
Project Overview

An online retailer needing smarter product discovery experiences to match diverse customer preferences and growing product catalog challenges common in modern e-commerce.

The Challenge

Legacy product suggestion tools delivered irrelevant and static recommendations that frustrated users and missed upsell chances.

  • Irrelevant product recommendations reducing engagement
  • Limited understanding of product relationships
  • Poor handling of new or niche products
  • Static, rule-based systems unable to adapt to preferences
  • Scalability issues with large catalogs

Our Solution

Softblues built an AI-driven recommendation engine that uses language models and smart matching to personalize product suggestions in real time. It learns from behavior, accounts for product relationships, and adapts to changing trends and contexts across large catalogs.

  • Real-time personalized product recommendations
  • Context-aware suggestion logic
  • Cross-product relationship mapping
  • Dynamic preference adaptation
  • Support for A/B testing and analytics
Technology

Built with Enterprise-Grade Technology

OpenAI GPT-4LangChainRedisPostgreSQLElasticsearchMachine LearningCustomer Behavior TrackingAPI Integrations
Client Goals

Goals and Objectives

The client came to us with clear objectives to transform their operations.

01

Improve Recommendation Relevance

Provide recommendations more aligned with customer intent and preferences.

02

Increase Product Discovery

Help customers find relevant products quickly, improving engagement metrics.

03

Boost Cross-Selling

Enable smarter cross-product suggestions that increase average order value.

04

Enable Real-Time Personalization

Adapt product suggestions based on live user behavior and context.

Platform Architecture

How It All Works Together

1

Data Processing Layer

Indexes product catalog and tracks customer behavior for real-time personalization.

2

AI Recommendation Engine

Uses embeddings and semantic matching to map products and deliver relevant suggestions.

3

Integration Layer

Connects with the e-commerce platform, analytics tools, and front-end for delivery.

Results

Value and Impact Delivered

Measurable improvements across every dimension of operations.

80%

Better Relevance

Recommendation relevance improved significantly compared to static systems.

50%

More Discovery

Customers discovered more relevant products leading to increased engagement.

40%

Cross-Sell Boost

Smarter cross-selling contributed to higher order values.

95%

Accuracy

High accuracy in recommending products that customers engaged with.

Ready to Transform Your E-commerce & Retail Operations?

See how AI can help your organisation reduce errors, speed up processing, and improve outcomes. Let's discuss your specific challenges.

Book Discovery Call
15+
Years Experience
200+
Projects Delivered
$1M
Insurance Coverage
Success Stories

Explore Other Projects

Discover more AI solutions delivering measurable results across industries