Overview
Our client operates one of the leading SaaS platforms specializing in personalized eCommerce experiences at massive scale, serving some of the largest global brands in the eCommerce domain.
In today's digital commerce landscape, customer experience and personalization have become the primary drivers of digital sales success. Customers expect 24/7 excellence, requiring online businesses to deliver perfect experiences continuously. Product discovery - the process of presenting the right product to the right customer in the right context, emerged as one of the most crucial elements of customer experience. However, traditional text-based search engines failed when the customer's language didn't match the product catalog terminology.
When Generative AI innovation began transforming industries, our client saw an opportunity to completely redefine eCommerce product discovery. They realized that GenAI could bring semantic understanding and contextual intelligence to create truly meaningful shopping experiences. To achieve this vision, they needed to rebuild their core discovery engine using a comprehensive GenAI stack, requiring specialized expertise in data science and AI engineering that led them to partner with us.
20%
Increase in ROI within
a year
55%
Reduction in manual merchandising effort
98%
Customer satisfaction
score
Architecting the Core GenAI Discovery Engine
We approached this challenge by designing a modular AI architecture that could serve as the intelligent foundation for all discovery experiences rather than creating point solutions for individual problems. Our execution involved systematically replacing legacy components with a sophisticated system leveraging multiple large language models, advanced vector databases, and semantic embedding technologies to create a platform that truly understands both products and customer intent at unprecedented levels.
The implementation required close collaboration with their existing engineering team while building scalable infrastructure that processes millions of customer interactions simultaneously with sub-second response times. Our vector embedding approach transforms product attributes and customer preferences into mathematical representations that enable semantic similarity matching, allowing the system to understand complex product relationships and customer needs. The core engine employs intelligent ranking models that continuously learn from interactions and purchase patterns to optimize product positioning while automatically enhancing product data.
When Search Understands What You Really Mean
The search engine analyzes search terms alongside broader context, including customer history, seasonal relevance, and browsing patterns, to surface highly relevant products. Advanced natural language processing identifies subtle variations in intent, distinguishing between research-focused and purchase-ready searches to adjust result presentation accordingly, enabling customers to use conversational phrases.
Visual Search That Sees What You See
The system seamlessly combines visual and textual search inputs, allowing customers to upload an image while adding text descriptions like "similar style but in blue" or "same aesthetic under $100." Advanced algorithms understand fashion trends, design aesthetics, and product categories to suggest not just visually similar items but products that match the overall style or functional requirements implied by the search query, creating a truly intuitive experience.
Key Features
Recommendations That Feel Like Mind Reading
The recommendation engine intelligently selects the most effective approach for each customer interaction, whether highlighting complementary products, suggesting upgrades, or introducing new product categories based on preferences. Advanced algorithms identify unexpected product relationships and cross-category opportunities, helping customers discover relevant items they might not have considered while driving increased order values through strategic product combinations that feel natural.
AI-Powered Employee That Never Sleeps
We developed intelligent AI agents that automate complex merchandising tasks traditionally requiring extensive manual analysis and decision-making by eCommerce teams. These agents continuously monitor performance data across thousands of products, identify optimization opportunities, and automatically implement improvements to product positioning, promotional strategies, and inventory management without human intervention.
The AI merchandising system automatically adjusts product rankings, modifies promotional displays, and optimizes category presentations based on real-time performance data and predictive analytics. The agents identify complex patterns and opportunities that would be difficult for human analysts to detect, such as subtle seasonal preference shifts, emerging trend opportunities, or cross-category promotional strategies, eliminating guesswork and manual effort while maximizing revenue opportunities through intelligent automation.
Tailor Every Experience Perfectly in Real-Time
The system provides brands complete control through customizable GenAI algorithms, allowing them to adjust attribute weightings, modify ranking factors, and configure parameters, including price sensitivity & category-specific criteria. This ensures AI delivers experiences that reflect each brand's unique value propositions while maintaining their specific business logic and customization rules.
Impact
The successful implementation of Generative AI positioned our client as an innovation leader in the eCommerce technology space. It attracted new enterprise customers specifically seeking advanced AI-powered discovery capabilities, while also providing a scalable foundation for ongoing enhancement and market expansion. eCommerce brands using the new AI-driven discovery platform report higher ROI compared to traditional search technologies, demonstrating significant business impact.
20%
Increase in ROI within a year
50%
Reduction in manual merchandising effort
92%
Customer satisfaction score
Want to double your conversions with GenAI-powered product discovery, just like their customers did?
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