How MLtwist Supported a Retail Analytics Platform in Structuring Product Data at Scale

The Use Case

Data driven retail platforms depend on clean, structured product information to generate accurate insights for brands, retailers, and supply chain partners.

A fast growing retail analytics company aggregates sales and inventory data across thousands of independent stores. To deliver meaningful insights, the platform needed a highly structured product catalog where every item was categorized and subcategorized consistently across brands, product types, and retail segments.

However, the company’s database contained hundreds of thousands of products collected from many different sources, each using inconsistent naming conventions, descriptions, and classification standards.

The company partnered with MLtwist to transform its raw product listings into a reliable, standardized taxonomy that could power analytics, reporting, and decision making across its platform.

THE CHALLENGE

Building a Reliable Product Categorization System at Massive Scale

Creating a structured product database presented several real world challenges:

• Unstructured product data: Product names and descriptions varied widely, often lacking the detail needed for accurate categorization.
• Inconsistent classifications: Similar items were labeled differently across brands and retailers, preventing meaningful comparisons.
• Verification requirements: Ensuring each product was placed in the correct category and subcategory required manual validation using external sources.
• Scale and speed: Categorizing hundreds of thousands of items manually would have been prohibitively slow without technological acceleration.
• AI limitations: Large language models could accelerate classification but required human oversight to detect and correct hallucinations or misclassifications.

MLtwist’s Solution: AI Enabled Categorization with Human Validation

MLtwist delivered an end to end solution combining its platform technology, AI acceleration, and a specialized workforce.

AI Powered Kickoff: MLtwist leveraged AI models to generate initial product categorizations at scale, dramatically reducing processing time.

Human in the Loop Validation: A trained workforce reviewed each item, researching products online to confirm accurate categories and subcategories.

External Verification: Specialists validated classifications against real world product information from manufacturer sites and retailer listings.

Hallucination Correction: Human reviewers identified and corrected errors introduced by automated models, ensuring reliability.

Taxonomy Standardization: MLtwist established a consistent categorization framework that could scale as new products entered the system.

Quality Assurance Layers: Multi step review processes ensured consistency and accuracy across the entire catalog.

Impact & Benefits

Massive Catalog Structuring: Hundreds of thousands of products were categorized into a unified taxonomy.

• Higher Quality Insights: Clean product data enabled more accurate analytics and reporting across retailers and brands.
• Faster Time to Value: AI acceleration combined with human validation delivered results significantly faster than manual efforts alone.
• Scalable Process: The company gained a repeatable workflow for categorizing new products as they enter the platform.
• Improved Data Reliability: Human oversight ensured trustworthy outputs suitable for business critical decisions.

The Takeaway

MLtwist’s combination of AI driven processing and expert human validation transformed a fragmented product database into a structured intelligence layer. By correcting AI hallucinations and verifying classifications against real world information, MLtwist enabled the retail analytics platform to deliver more accurate insights and scale its operations with confidence.