AI-Driven Data Engineering That Transformed 10,000+ Stock Cards
How we deployed a custom AI-powered data cleansing framework that turned a fractured data ecosystem into a structured, intelligent foundation—without interrupting operations.
A Major Player in Food & Beverage Distribution
Our client operates across both B2B and B2C channels, supplying non-perishable goods to a vast customer base across the United Kingdom. With over 10,000+ unique SKUs and a substantial operational footprint, the business depends heavily on clean, reliable data for inventory management, order processing, and customer relationship workflows.
All operations are centralized through a complex ERP system integrated with legacy spreadsheets and CRM platforms—a common scenario that creates data chaos at scale.
A Data Ecosystem Breaking Down
Despite their scale, the client's data ecosystem was causing material business issues that impacted operations daily.
Misaligned Product IDs
Product identifiers varied across ERP, CRM, and legacy spreadsheets with no single source of truth
Duplicate Records
Same customers, suppliers, and products existed multiple times with inconsistent data
Non-Standard Formats
Dates, addresses, SKUs, and units followed different formats across systems
Missing Fields
Critical data gaps across thousands of records causing operational delays
Business Impact
AI-Powered Data Cleansing Framework
OARC deployed a custom AI-powered data cleansing framework designed to clean, standardize, and strengthen the client's data architecture—without interrupting operations.
Data Audit Across Systems
Mapped inconsistencies across ERP, CRM, and offline databases to build a unified data model that became the single source of truth.
AI-Led Record Standardization
NLP models normalized product names, supplier codes, and category formats. Formatting rules applied for units of measure, addresses, SKUs, and date structures.
Fuzzy Matching & De-duplication
Sophisticated data matching techniques identified and merged duplicate records across product, customer, and supplier datasets.
Rule-Based Validation Engine
Built custom rules (valid SKU patterns, category logic) to flag anomalies and incomplete fields in real-time.
Enrichment and Completion
Leveraged external data sources to auto-complete address fields and supplier data, filling gaps across thousands of records.
Real-Time Data Quality Dashboards
Deployed dashboards giving leadership full visibility into data health metrics across systems, supporting ongoing governance.
Core Capabilities Delivered
NLP-Powered Standardization
AI models normalized product names, supplier codes, and category formats across 10,000+ SKUs
Fuzzy Matching & De-duplication
Sophisticated algorithms identified and merged duplicate records across product, customer, and supplier datasets
Rule-Based Validation Engine
Custom rules flagged anomalies, invalid SKU patterns, and incomplete fields automatically
Real-Time Quality Dashboards
Live visibility into data health metrics across all systems for ongoing governance
From Bottleneck to Business Enabler
In just weeks, OARC transformed a fractured data ecosystem into a structured, intelligent foundation that enabled business growth.
Dramatic Reduction in Order Errors
Standardized product and supplier records improved system accuracy and reduced fulfillment mistakes
Stronger Forecasting & Reporting
Clean, unified data allowed for precise inventory tracking and more accurate demand planning
Faster Operations
Team members no longer slowed down by inconsistent records, duplicates, or manual corrections
Foundation for Scalable AI
Clean dataset now enables AI automation across supply chain, pricing, and customer experience
"What was once a bottleneck is now a business enabler—driven by OARC's ability to combine AI, data engineering, and strategic problem-solving in one seamless solution."
Operations Director, F&B Distribution Client