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Church's Chicken
Read | Jul 02, 2025
AUTHOR(s)
A WNS Perspective
WNS harnessed AI and NLP to slash grocery backend effort, powering faster, smarter customer experiences.
WNS collaborated with a leading global food delivery platform to improve the efficiency of its grocery ordering backend. By embedding AI and NLP into the product linking process, the platform significantly reduced manual effort, improved search accuracy and created a scalable knowledge base for high-quality customer experiences.
~% Efficiency Improvement by reducing manual effort for matched cases
Improved search accuracy through product annotation and smart suggestion tagging
Enhanced model performance by integrating feedback into automation logic
Standardized knowledge base for consistent interpretation across item types
Faster and smarter grocery ordering experience, driving better CX and operational throughput
The grocery ordering system depended on intensive manual effort to match user-entered product names with entries in the internal database. Variations in product descriptions, quantities and brand names led to inefficiencies, delays and limited scalability in delivering relevant suggestions to users.
Streamlined the product linking process with intelligent logic for product name, quantity and pack size matching
Introduced decision rules for edge cases: No match, multiple matches and queueing for review
Applied NLP-based classification and tokenization for semantic similarity detection
Leveraged WNS Dataturf.ai for contextual data cataloging and insight generation
Designed a human-in-the-loop mechanism where rejected cases were manually validated and re-integrated into the AI engine
Efficiency at scale demands intelligent matching, structured learning and contextual nuance. WNS helped the client build a resilient backend for grocery ordering that delivers accuracy, speed and smarter outcomes powered by AI and human oversight.
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