Introduction

Blue Bungalow, one of Australia’s leading fashion retailers, faced a common challenge in eCommerce—how to create a more engaging, seamless, and personalised shopping experience for customers online. They wanted to implement AI-powered assistance to provide personalised product recommendations, accurate sizing guidance, product comparisons, and instant answers to customer questions—replicating the ease and support of an in-store shopping experience. To bring this vision to life, preezie developed an AI shopping assistant, built on Semantic Kernel and Elasticsearch.

The challenge: Enhancing the online shopping experience

Blue Bungalow faced several key challenges in improving customer engagement:

• Navigating a large product catalogue – Customers needed an efficient way to quickly find relevant products.
• Enhancing customer support – Frequently asked product-related queries risked overwhelming human support teams.
• Improving product discovery – Customers often spent time searching for alternatives, similar styles, or comparative options.

The solution: preezie’s AI shopping assistant

preezie developed a conversational AI assistant that acts as a virtual shopping guide. The solution is powered by Semantic Kernel, a framework that enables AI-driven orchestration and automation, making the assistant more intelligent and adaptive.

The AI assistant enhances Blue Bungalow’s website with the following functionalities:

1. Intelligent search and discovery

The assistant enables users to search for products using natural language processing (NLP). Semantic Kernel plays a crucial role in interpreting search intent and retrieving relevant results. Customers can interact with the AI as if they were speaking to a real person, using queries like:

• “Show me floral dresses for summer.”
• “Find me a red evening dress under $100.”
• “I need shoes that match this outfit.”

The system then queries Elasticsearch to fetch the most relevant results, while Semantic Kernel refines responses by incorporating contextual understanding and reasoning.

2. Finding similar products

A core feature of the AI assistant is its ability to suggest alternatives that closely match customer preferences. Semantic Kernel enhances similarity detection by leveraging embeddings and contextual analysis, ensuring that recommended products align with individual shopping behaviours.

The assistant can:

• Retrieve similar items based on colour, style, and price.
• Offer substitutes when an item is unavailable.
• Provide visually similar products using integrated image recognition models.

3. Product comparisons

To help shoppers make informed purchasing decisions, the AI assistant enables side-by-side product comparisons. Powered by Semantic Kernel, it dynamically structures and summarizes key attributes, allowing users to:

• See key differences in fabric, fit, and price.
• Review customer feedback and ratings in a clear, easy-to-digest format.
• Explore alternatives across different price ranges, ensuring they find the best option within their budget.

Technology stack

preezie’s AI shopping assistant is powered by a cutting-edge tech stack designed for speed, intelligence, and scalability:

• Semantic Kernel – The core framework orchestrating AI-powered interactions, enabling contextual understanding and advanced automation.
• Elasticsearch – A vector database that powers fast, relevant, and precise product searches.
• C# and .NET Core – Ensuring a high-performance, scalable backend infrastructure.
• Azure – Providing robust cloud infrastructure and leveraging OpenAI models for enhanced AI capabilities.

Impact and results

The implementation of preezie’s AI shopping assistant led to significant improvements in customer engagement and conversions on Blue Bungalow’s website. Comparing customers who interacted with the AI assistant to those who did not, the following results were observed:

• ⏳ Time spent on site: Doubled among AI-engaged customers.
• 🛒 Add-to-cart rate: 40% higher among AI-engaged customers.
• 📈 Conversion rate: Increased by 85% to 110% among AI-engaged customers.
• 💰 Average Order Value (AOV): 7% higher among AI-engaged customers.

Demo of our AI shopping assistant

🤖 preezie’s AI shopping assistant is now more versatile than ever! 👏🏽🛍

Conclusion

The preezie AI Shopping Assistant has transformed the online shopping experience for Blue Bungalow, enhancing customer engagement, product discovery, and sales conversions. Powered by Semantic Kernel, this AI-driven solution delivers hyper-personalised product recommendations, precise size guidance, and seamless product comparisons—helping shoppers browse, compare, and buy with confidence, all in real-time.

As preezie continues to push the boundaries of AI in e-commerce, Semantic Kernel remains a core pillar of its innovation strategy. Looking ahead, preezie is excited to explore new capabilities that will further enhance the preezie AI platform, making online shopping even more intuitive, personalised, and engaging