The client relied heavily on manual effort to match user-entered product names with items in their internal grocery database. Frequent variations in product descriptions, quantities, brands, and formats led to inconsistent search suggestions, higher operational overhead, and slow response times. This made it difficult to scale operations efficiently and maintain a seamless customer experience.

WNS implemented an AI-driven matching framework combined with NLP techniques such as tokenization, semantic similarity detection, and classification. These capabilities enabled automated interpretation of product names, pack sizes, and quantities. Intelligent decision rules helped handle edge cases like no match or multiple matches. Together, these innovations significantly minimized manual intervention and improved accuracy.

The solution delivered a ~40% increase in operational efficiency, mainly by reducing manual review for matched cases. Automated product linking, improved model performance, and a streamlined workflow helped the client process more customer queries faster. This resulted in better throughput, consistent product suggestions, and improved customer satisfaction.

The "human-in-the-loop" approach ensures accuracy and continuous learning. When the AI engine could not confidently match a product, the case was routed to human reviewers for manual validation. These validated insights were then fed back into the AI model, enabling it to learn from mistakes, refine decision rules, and continuously improve performance over time.

WNS Dataturf.ai played a crucial role in organizing and structuring grocery catalog data. It enabled richer contextual insights, improved data cataloging, and supported the creation of a scalable knowledge base. This allowed the system to maintain consistent product interpretations across item types, brands, and categories—leading to better search relevance and more reliable recommendations.

Absolutely. The AI and NLP architecture is designed to be highly flexible and adaptable. The model can be trained for new regions, languages, and product catalogs with minimal effort. This makes the solution ideal for global grocery platforms or e-commerce businesses looking to streamline product matching, reduce operational costs, and enhance customer experience at scale.

Enhanced search accuracy ensures that customers quickly find the products they are looking for, even when item names are misspelled or incomplete. This reduces friction during ordering, shortens browsing time, and improves overall satisfaction. For the platform, better accuracy results in higher conversions, fewer abandoned carts, and a more efficient backend process.

By combining AI automation, NLP capabilities, and human oversight, the solution builds a future-ready backend that learns and improves continuously. It reduces operational bottlenecks, ensures consistent product mapping, and supports intelligent decision-making. This allows the client's grocery operations to scale efficiently while maintaining accuracy, reliability, and speed.