Retail Client
AI-driven data engineering to redefine over 10,000 stock cards for a national food and beverage distributor.
Client overview
A major player in the food and beverage distribution space, this client operates across both B2B and B2C channels, supplying non-perishable goods to a wide customer base. With over 10,000+ unique SKUs and a vast operational footprint, the business depends heavily on clean, reliable data for inventory management, order processing, and customer relationship workflows. All operations are centralised through a complex ERP system integrated with legacy spreadsheets and CRM platforms.
The challenge
Despite their scale, the client’s data ecosystem was breaking down.
They were dealing with unstructured, inconsistent, and low-quality data across critical systems—ERP, CRM, and historical spreadsheets. Product IDs were misaligned. Fields were missing. Supplier and customer records were duplicated. Formats were non-standard across categories like dates, addresses, and SKUs.
This caused a series of material business issues:
- Frequent order errors
- Delayed fulfilment and shipping
- Inaccurate inventory forecasting
- Poor reporting visibility
The Solution: AI-Powered Data Cleansing Model
Bullshark deployed a custom AI-powered data cleansing framework designed to clean, standardise, 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.
AI-Led Record Standardisation
- NLP models were used to normalise 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 were deployed to identify and merge duplicate records across product, customer, and supplier datasets.
Rule-Based Validation Engine
- Built custom rules (e.g. valid SKU patterns, category logic) to flag anomalies and incomplete fields.
Enrichment and Completion
- Leveraged external data sources to auto-complete address fields and supplier data, filling gaps across records.
Real-Time Data Quality Dashboards
- Deployed a set of dashboards that gave leadership full visibility into data health metrics across systems, supporting ongoing governance.
The outcome
In just weeks, Bullshark transformed a fractured data ecosystem into a structured, intelligent foundation that enabled business growth.
Dramatic Reduction in Order Errors
Standardised product and supplier records improved system accuracy and reduced fulfilment mistakes.
Stronger Forecasting & Reporting
Clean, unified data allowed for precise inventory tracking and more accurate demand planning.
Faster Operations
Team members were no longer slowed down by inconsistent records, duplicates, or manual corrections.
Foundations for Scalable AI & Automation
With a clean dataset, the client is now positioned to roll out further AI automation across supply chain, pricing, and customer experience.
What was once a bottleneck is now a business enabler—driven by Bullshark’s ability to combine AI, data engineering, and strategic problem-solving in one seamless solution.