Success Story

The client is a food delivery and hybrid food hall platform.

With its curated collection of the best chefs and well-known restaurants from across the nation, the client enables customers to explore food options and place orders from its single consolidated platform.

The client sought to elevate customer experience by allowing users to effortlessly plan their weekly meals through personalized recommendations and a seamless ordering process.

To achieve this, they aimed to develop a personalized meal planning recommendation engine capable of interpreting complex customer behavior and contextual signals. 

By doing so, the client intended to boost customer retention and expand its market share by encouraging repeat orders.

Another key objective was to reduce manual intervention by driving automation across processes.

We initiated our engagement by collecting user data such as order history, preferences, demographics, contextual information and pre-processing it for AI modelling.

Our team captured this data by utilizing deep learning models.

We integrated a hybrid recommendation system consisting of collaborative and content-based filtering in our solution.

This enabled the application to recommend food options based on similar user behavior and personal food preferences.

To offer personalized meal selection and contextual recommendations, we integrated our solution with a Gemini-powered conversational chatbot and a seamless UI.

The chatbot used deep learning techniques like RNNs, Transformers, and two-tower networks to further enhance personalization.

We trained the models with metrics such as Precision, Recall, F1, and MRR, and refreshed them regularly with new data to improve dynamic machine learning capabilities.

This drove sales through improved user engagement and kept the recommendations relevant.

On the deployment front, our team utilized cloud infrastructure to ensure real-time recommendations, seamless scalability and thus continuously improved model accuracy.

Improved personalization led to stronger customer loyalty and 20% increase in repeat orders.

We increased customer engagement by 25% with tailored meal plans and intuitive UX.

Our solutions led to 10% faster menu optimization with real-time feedback loops and data-driven insights.

We reduced the manual effort by 70% by significantly reducing the support workload through automated data cleaning and tagging.

The client gained an impressive 15% market share and expanded its footprint while outperforming competitors.