Personalized E-Commerce Experiences: Enhancing Product Recommendations with Amazon Bedrock using Amazon Titan Multimodal Embeddings

Introduction:

In today’s fast-paced e-commerce environment, personalization is no longer optional—it’s a necessity for driving customer engagement and revenue. One of the top 10 global fashion e-commerce companies partnered with Texple to transform its legacy recommendation engine that failed to adapt to user intent in real time.

Texple implemented an AI-driven solution leveraging Amazon Titan Multimodal Embeddings via AWS Bedrock, delivering personalized, context-aware recommendations that improved the customer experience and core business metrics.

Requirements:

• Create a responsive recommendation engine that understands both textual and visual product attributes

• Adapt suggestions based on real-time user behavior and intent shifts

• Enable users to discover products via both text and image inputs

• Improve user engagement and reduce cart abandonment

• Integrate seamlessly with the client’s existing AWS infrastructure

Challenges:

The client’s existing product recommendation system relied on traditional filtering techniques and simple logic, resulting in a static, one-size-fits-all experience that was no longer sufficient for modern user expectations.

Key limitations included:

• Lack of personalization: Recommendations were based on general categories with little variation across users

• Inability to understand visual or textual context: No comprehension of detailed product attributes

• No dynamic updates: In-session preference changes weren’t reflected in real time

• Weak discovery experience: Complex or multi-attribute queries returned poor results

These limitations led to lower engagement, higher bounce rates, and reduced customer retention.

Solution:

Texple built a next-generation product recommendation engine using Amazon Titan Multimodal Embeddings hosted on AWS Bedrock. The system integrated real-time behavioral data, natural language understanding, and image recognition to power intelligent product discovery.

Architecture

Approach

Step 1: Multimodal Product Understanding
Texple used Amazon Titan to generate embeddings for each product by processing:

• Product descriptions

• Product images

• Structured metadata

This enabled the system to suggest products based on deeper features such as color, texture, and design, rather than only by category.

Step 2: Real-Time Behavioral Personalization
The AI engine monitored user activity—clicks, searches, time spent on product pages, and cart behavior—and adjusted recommendations in real time. For example, if a user shifted from browsing leather boots to hiking shoes, the recommendations adapted accordingly.

Step 3: Enhanced Search and Visual Discovery
The platform supported both keyword and image-based searches. Users could upload photos or enter specific queries like “red memory foam running shoes,” and receive accurate matches based on both visual and textual cues.

Step 4: Personalized Homepages and Targeted Promotions
Texple implemented AI-driven homepage modules that changed dynamically based on:

• Browsing and shopping history

• Abandoned cart contents

• Preferred product categories

Services Implemented

• Amazon Bedrock for serverless deployment of foundation models

• Amazon Titan Multimodal Embeddings for image and text processing

• Amazon S3 for storing product metadata and session logs

• Amazon CloudWatch for performance monitoring

• AWS Lambda and API Gateway for real-time API-driven personalization

• Optional integration with Amazon Personalize for collaborative filtering

Benefits Achieved

• 40% increase in conversion rates due to smarter recommendations

• 60% faster product discovery via improved search experiences

• 35% higher customer retention through engaging, personalized interfaces

• 50% more interaction with visual search tools

• 24% growth in average order value (AOV)

• Noticeable decrease in cart abandonment driven by timely deal prompts

Conclusion

Texple’s AI-powered personalization solution, backed by Amazon Titan and AWS Bedrock, enabled the client to significantly modernize its e-commerce experience. The unified recommendation engine, combining multimodal understanding with behavioral insights, delivered meaningful results: higher conversions, better retention, and a more satisfying customer journey.

If your organization is ready to scale intelligent personalization with enterprise-grade AI, Texple is the partner to make it happen.