Buying furniture online should feel personal, not overwhelming. But traditional eCommerce experiences are often generic, time-consuming, and frustrating, leaving users to browse endlessly with little confidence in their choices. To change that, SeededHome partnered with GoML to build a conversational AI shopping assistant tailored for the furniture space.  

The problem: complex journeys, generic results, and decision fatigue

Despite having a curated catalog of furniture and home decor, SeededHome faced several challenges that hindered user satisfaction and conversion. The buying journey was often long and fragmented, requiring users to manually browse through countless products and filters. This created decision fatigue, where users became overwhelmed by choices and abandoned their carts before completing a purchase. Additionally, the platform lacked true personalization, recommendations were based on broad categories rather than individual preferences or styles.  

As a result, users felt disconnected from the experience. SeededHome needed a smarter way to guide users through the shopping process, one that could understand intent, adapt in real-time, and deliver tailored product matches. A personalized AI shopping assistant was the missing link to unlock faster, more confident buying decisions while scaling a truly delightful user experience.

The solution: a conversational AI shopping assistant for furniture

GoML helped SeededHome design and implement a hyper-personalized AI shopping assistant using advanced Generative AI models, natural language processing, and AWS-native infrastructure. This assistant offers tailored recommendations, reduces shopping time, and builds user trust through relevance and simplicity.

Immersive preference mapping

The AI shopping assistant begins by building a detailed persona based on user style, space constraints, budget, and lifestyle needs, enabling product recommendations that feel made-to-order.

Intelligent product matching

Using recommendation algorithms and ranking engines, the assistant suggests the most relevant items from SeededHome’s catalog, shortening the path to purchase.

Conversational interface powered by NLP

Built with Claude on Amazon Bedrock, the assistant supports fluid English conversations like “Show me minimalist beds under ₹20,000,” offering human-like interactivity within the furniture shopping flow.

The impact: Delightful experience with AI shopping assistant

Happier Customers

AI-driven personalization reduces stress and choice paralysis, helping users feel more confident and satisfied with their picks.

Boosted Sales

Faster, more relevant product discovery leads to shorter journeys and higher conversion rates.

Market Leader Positioning

By pioneering an AI-powered personalized shopping assistant for furniture, SeededHome differentiates itself in a crowded retail space, offering an experience users didn’t know they needed, but now can’t shop without.

Lessons for eCommerce, lifestyle, and D2C platforms

Common pitfalls to avoid

• Treating all shoppers the same with static recommendations

• Ignoring emotional and contextual triggers in furniture buying

• Relying on traditional filters instead of AI-driven personalization

Tips for product and growth teams

• Build user personas through conversational interfaces to deepen engagement

• Use vector search (Weaviate) and embedding models for high-relevance matches

• Combine NLP and behavioral data to recommend not just what's available—but what's right

Ready to build an AI shopping assistant your users will love?

Let GoML help you build an AI-powered personalized shopping assistant for furniture that transforms browsing into buying, with empathy, intelligence, and speed.

Reach out to build your Gen AI-powered retail experience.