Our customer is an Indian-based PlayTech e-commerce startup that aims to change the way in which parents, educators, and gift-givers learn about the appropriate toys. The platform is based on both B2C and B2B models as it provides users with the opportunity to buy toys, while allowing the sellers to put their items on sale and sell them within the same ecosystem. The client was highly concerned with learning, skill development, and inclusive play and tried to use AI-driven technology to ensure that the process of toy discovery in their mobile application became more intuitive, personalized, and efficient.
Python
Hugging Face
React Native
FastAPI
AWS
The client wanted to enhance the experience of shopping as a whole by eliminating the friction in product discovery and making toy search more intelligent and relevant to users. They wanted to
The client was interested in improving its search engine to facilitate the discovery of toys and make it simpler and easier to use. They, however, struggled to go beyond basic keyword-based search because it was not very good at interpreting user intent, detailed queries, and conversational search queries.
They had a huge toy collection, and it was hard to navigate. The difficulty of filtering options caused many users to get confused and spend more time on searches, and abandon the search without results.
It was not possible to get recommendations according to the age, interests, and learning needs of a child on the platform. Consequently, users were presented with generic results rather than suggestions that actually addressed their needs.
This new enhancement needed to integrate well with the current Shopify system and mobile application. The issue was to introduce superior features without compromising functionality, accuracy of data, and the user experience.
We focused on fixing what truly mattered. We made search easier, recommendations more relevant, and the overall experience smooth across the app. Our solution was built to feel simple for users while staying efficient and reliable behind the scenes.
The emphasis was put on ensuring that the search was natural and easy. Rather than making users use precise keywords, we enhanced the search to be able to read intent and deal with the detailed conversational searches just as people are natural searchers.
We created a recommendation layer that assists users in finding toys according to their actual needs, including age, interests, and learning objectives. This was less time wastage since you could easily browse through options that had no relevance to you.
We have linked the solution with the Shopify backend to ensure that the information about the products is always accurate. This made prices, availability, and product updates never be out of sync, and all without manual effort.
The whole solution was made to blend into the existing mobile application. We created lightweight, scalable APIs and included them in the React Native application without compromising performance and user experience.
Users were no longer required to make guesses on the appropriate keywords. Even more specific or lengthy search options began to present the relevant toy options, and it became easier to get the toy that they wanted.
With smarter suggestions in place, users spent less time browsing and more time discovering toys that matched their needs. The path from search to selection became much shorter.
When users found the right toys faster, they were more confident in making a purchase. This directly reflected in higher engagement and improved conversion rates.
The overall flow of searching and exploring toys felt simple and effortless. Users enjoyed a more engaging and frustration-free shopping experience on the mobile app.
With automated search logic and real-time product updates, the internal team spent less time managing data and more time focusing on growth.
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