Customer Stories
AI automation transforming catalog accuracy and speed
AAI-Driven Category Mapping System for mcgrocer.com to Support Real-Time Product Catalog Expansion.
About the Organization
mcgrocer.com is a fast-growing UK-based online grocery aggregator partnering with multiple supermarkets, wholesalers, and specialty retailers. Their platform hosts hundreds of thousands of products sourced from various vendors, each using different naming conventions and category structures.
As the business scaled, maintaining a clean, consistent, and searchable product catalog became critical to delivering a smooth customer experience
With thousands of new products added from multiple UK retailers, mcgrocer.com needed a way to keep its catalog accurate, structured, and up to date. Instead, inconsistent source categories and manual intervention led to delays, classification errors, and heavy operational overhead.
As the catalog scaled toward 200,000+ SKUs, product discovery suffered, navigation became unreliable, and internal teams struggled to maintain taxonomy quality. Manual mapping was no longer sustainable — exposing the need for a faster, scalable, and intelligent approach.
Why
An AI-powered Automated Category Mapping System was built to analyze product data and map each item into
mcgrocer.com’s redesigned 8-level taxonomy.
The system ensures every SKU is classified accurately and consistently — without human intervention —
enabling real-time catalog updates across all vendor partners.
What
The platform uses a combination of LLM-based semantic understanding and vector similarity scoring to
interpret product titles, descriptions, and attributes. It automatically determines the most accurate
placement within mcgrocer.com’s multi-level taxonomy and validates results through confidence scoring
and rule-based checks.
Batch automation, continuous learning, and Azure-native deployment allow the system to process large
volumes of new products instantly, ensuring the catalog remains updated as vendors push fresh inventory
How
Before the Project
mcgrocer.com relied on brute-force scraping to collect product data from partner stores
Each store used different category names and structures, which meant:
- Products frequently appeared in wrong or irrelevant categories
- New items had to be manually reviewed and reassigned
- Taxonomy alignment was inconsistent across vendors
- Manual teams spent hours mapping SKUs one by one
- Catalog updates were slow and error-prone
- Search and navigation suffered, impacting user experience
With the catalog scaling to over 200,000 products, maintaining quality manually was no longer feasible.
The Solution
- AI-Powered Category Mapping Engine: The system autonomously analyzes titles, descriptions, ingredients, brand names, and attributes to identify the correct taxonomy path.
- 8-Level Taxonomy Alignment: Each product is mapped across all levels — from broad department to the deepest sub-category — ensuring consistent classification across the platform.
- LLM + Vector Similarity Hybrid Approach: Combines semantic understanding with similarity scoring to achieve precise, context-aware results.
- Automated Pipelines and Batch Processing: New products are processed instantly as they arrive from vendors, eliminating delays and manual sorting.
- Validation Logic and Confidence Scoring: Built-in rules ensure high accuracy, flagging only uncertain items for review.
- Continuous Learning: The engine improves its accuracy over time by learning from corrections and historical patterns.
- Azure-Deployed Architecture: All processing, classification, and scaling workloads run on Azure for reliability, security, and seamless scalability.
The Impact
The automated category mapping system transformed mcgrocer.com’s catalog operations:
- 95%+ classification accuracy across all category levels
- Hours instead of months to classify large batches of products
- Near real-time updates for new items from partner stores
- Consistent taxonomy across 200,000+ SKUs
- Improved product discovery and navigation for shoppers
- Reduced operational workload for manual teams
- Future-ready scalability as new vendors and categories are added
The solution modernized mcgrocer.com’s entire product-catalog workflow, enabling them to grow rapidly while maintaining a clean, accurate, and searchable catalog for customers.
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