Cainz Adopts Lazuli PDP for PoC on Product Data Maintenance for Marketing
Lazuli PDP has been successfully utilized in a product data maintenance PoC for Cainz. The companies are now moving to expand the scope for full-scale implementation.
📋 Article Processing Timeline
- 📰 Published: May 25, 2026 at 20:00
- 🔍 Collected: May 25, 2026 at 11:31
- 🤖 AI Analyzed: May 26, 2026 at 06:00 (18h 29m after Collected)
Lazuli Inc., a provider of the product data operation platform 'Lazuli PDP (Product Data Platform)' utilizing AI technology, announced the successful completion of a proof-of-concept (PoC) for product data maintenance at Cainz Corporation. Following the success of this PoC, both companies have moved to an expanded PoC covering multiple categories with a view toward company-wide implementation and operation.
Background and Objectives
Cainz is committed to continuously improving the customer experience through its EC site and owned media. To realize personalization strategies that propose the optimal product for each customer, and to effectively link articles with products, it is essential that product data is equipped with customer-oriented attributes and feature tags. However, it is realistically difficult to manually maintain such tags for an enormous volume of products. This PoC was conducted to verify whether this process can be automated using AI.
Outline and Results of the PoC
In this PoC, Lazuli targeted categories where data maintenance is particularly difficult, validating category estimation and the automatic generation of feature tags by AI. By performing category estimation and tag assignment not only on the product master but also on content from Cainz's owned media, the company built a system where products and content can be understood across a common axis. This established a foundation for marketing strategies that link content with products based on customer interests.
Furthermore, rather than keeping the generated tags as they are, the system adopted a format that assigns a score to each tag indicating the strength of its connection with the product. This has built a foundation that allows for flexible prioritization in future search accuracy improvements and recommendation utilization.
Future Developments
Following the successful PoC, the companies have moved to additional verification with an expanded scope of multiple categories. They are accelerating the verification by rapidly deploying the common base for category estimation and tag generation, and are working in coordination with a view toward full-scale company-wide implementation and operation.
Through the value realization of product data using AI, Lazuli continues to support the digital transformation (DX) for Cainz's goal of 'making life fun around the world.'
Background and Objectives
Cainz is committed to continuously improving the customer experience through its EC site and owned media. To realize personalization strategies that propose the optimal product for each customer, and to effectively link articles with products, it is essential that product data is equipped with customer-oriented attributes and feature tags. However, it is realistically difficult to manually maintain such tags for an enormous volume of products. This PoC was conducted to verify whether this process can be automated using AI.
Outline and Results of the PoC
In this PoC, Lazuli targeted categories where data maintenance is particularly difficult, validating category estimation and the automatic generation of feature tags by AI. By performing category estimation and tag assignment not only on the product master but also on content from Cainz's owned media, the company built a system where products and content can be understood across a common axis. This established a foundation for marketing strategies that link content with products based on customer interests.
Furthermore, rather than keeping the generated tags as they are, the system adopted a format that assigns a score to each tag indicating the strength of its connection with the product. This has built a foundation that allows for flexible prioritization in future search accuracy improvements and recommendation utilization.
Future Developments
Following the successful PoC, the companies have moved to additional verification with an expanded scope of multiple categories. They are accelerating the verification by rapidly deploying the common base for category estimation and tag generation, and are working in coordination with a view toward full-scale company-wide implementation and operation.
Through the value realization of product data using AI, Lazuli continues to support the digital transformation (DX) for Cainz's goal of 'making life fun around the world.'
FAQ
What problems does Lazuli PDP solve?
It automates the consolidation, structuring, and normalization of unstructured product data dispersed across Excel, PDFs, etc., reducing operational burden.
What was validated in the PoC with Cainz?
AI-driven category estimation, automated generation of feature tags, and the construction of a marketing foundation that cross-links products with owned media content.
Which industries is Lazuli PDP suitable for?
It is suitable for a wide range of industries including brands, manufacturers, retailers, and distributors handling product information.