Data Generation

Reconstruct the Way it's Communicated,into Product Information that Maximizes value.

AI automatically collects product information published on the internet from multiple sources. Data in different formats such as Excel, PDF, and HTML is normalized to a common schema and converted into centrally managed structured data.
This automates competitive research and information gathering tasks that previously required manual effort and time.

Product Information Feature Tags

Automatically generates tags such as "Usage" and "Features" to enhance searchability and appeal.

Image Text Tags

Reads characters on packages and labels using OCR and converts them into structured data, such as brand, material, and capacity.

Image Feature Tags

Automatically assigns tags that extract product characteristics from photos and can be used for search and recommendations.

Product Description Generation

Generates natural sentences aligned with the brand and automatically checks for AI-specific exaggerated expressions.

Data Enrichment

Incomplete Information,Turned into "Usable Data".

AI automatically collects product information published on the internet from various sources in the market. In addition to data in different formats such as Excel, PDF, and HTML, it can also ingest information from standard specifications like GS1 and complex external services.
AI normalizes it to a common schema and further assigns attributes from Lazuli DB, converting it into centrally managed structured data.
This automates competitive research and information gathering, which previously required a vast amount of manual work, and allows immediate utilization of valuable product data.

Automatic completion of missing information (product name, category, etc.)

AI automatically complements blanks and unclear elements within product information. It eliminates the need for manual processing due to variations in notation or omissions, ensuring consistent master data.

Automatic expansion from JAN to GS1 and collected data

Based on the JAN code, it matches and retrieves product data collected by GS1 and Lazuli, automatically expanding the master data.

Feature Labeling

Based on product information, it can also automatically determine if it meets predefined criteria or conditions and assign it as a new attribute column.

Data Estimation

Derive Clear "Classifications" From Ambiguous Expressions.

AI automatically estimates and structures information embedded in product images and descriptions (such as category, color, material). It also complements variations in product names and missing tags, redefining them as structured data. This builds a foundation that can be utilized for more advanced data applications like segment analysis and recommendations.

Automatic Estimation of Product Category

AI automatically identifies the category to which a product belongs based on its name, description, image, etc.

Estimation of Attributes such as Product Color and Material

Based on image analysis and descriptive information, AI automatically classifies and extracts attributes such as color, material, and size. It also learns subjective expressions, enabling classification closer to the user's perspective.

Feature Estimation

Based on the features included in the product information, AI automatically estimates whether it matches predefined conditions such as "Food for Specified Health Uses" or "Gluten-Free" and assigns labels.

Data Collection

From "Collecting" to "Usable" Data.

AI automatically collects product information published on the internet from multiple sources. Data in different formats such as Excel, PDF, and HTML is normalized to a common schema and can be immediately utilized as centrally managed structured data. This automates competitive research and information gathering, which traditionally required a vast amount of manual work, and provides a data foundation directly linked to business in a copyright-free manner.

Automatic Collection from Multi-Data Sources

Collects product information from e-commerce sites, manufacturer sites, catalogs, etc.

Schema Unification and Normalization

Formats structures and notation variations from each data source into a unified format.

Real-time Updates

Constantly reflects the latest market information and automatically processes update differences.

Data Generation

Reconstruct the Way it's Communicated,into Product Information that Maximizes value.

AI automatically collects product information published on the internet from multiple sources. Data in different formats such as Excel, PDF, and HTML is normalized to a common schema and converted into centrally managed structured data.
This automates competitive research and information gathering tasks that previously required manual effort and time.

Product Information Feature Tags

Automatically generates tags such as "Usage" and "Features" to enhance searchability and appeal.

Image Text Tags

Reads characters on packages and labels using OCR and converts them into structured data, such as brand, material, and capacity.

Image Feature Tags

Automatically assigns tags that extract product characteristics from photos and can be used for search and recommendations.

Product Description Generation

Generates natural sentences aligned with the brand and automatically checks for AI-specific exaggerated expressions.

Data Enrichment

Incomplete Information,Turned into "Usable Data".

AI automatically collects product information published on the internet from various sources in the market. In addition to data in different formats such as Excel, PDF, and HTML, it can also ingest information from standard specifications like GS1 and complex external services.
AI normalizes it to a common schema and further assigns attributes from Lazuli DB, converting it into centrally managed structured data.
This automates competitive research and information gathering, which previously required a vast amount of manual work, and allows immediate utilization of valuable product data.

Automatic completion of missing information (product name, category, etc.)

AI automatically complements blanks and unclear elements within product information. It eliminates the need for manual processing due to variations in notation or omissions, ensuring consistent master data.

Automatic expansion from JAN to GS1 and collected data

Based on the JAN code, it matches and retrieves product data collected by GS1 and Lazuli, automatically expanding the master data.

Feature Labeling

Based on product information, it can also automatically determine if it meets predefined criteria or conditions and assign it as a new attribute column.

Data Estimation

Derive Clear "Classifications" From Ambiguous Expressions.

AI automatically estimates and structures information embedded in product images and descriptions (such as category, color, material). It also complements variations in product names and missing tags, redefining them as structured data. This builds a foundation that can be utilized for more advanced data applications like segment analysis and recommendations.

Automatic Estimation of Product Category

AI automatically identifies the category to which a product belongs based on its name, description, image, etc.

Estimation of Attributes such as Product Color and Material

Based on image analysis and descriptive information, AI automatically classifies and extracts attributes such as color, material, and size. It also learns subjective expressions, enabling classification closer to the user's perspective.

Feature Estimation

Based on the features included in the product information, AI automatically estimates whether it matches predefined conditions such as "Food for Specified Health Uses" or "Gluten-Free" and assigns labels.

Data Collection

From "Collecting" to "Usable" Data.

AI automatically collects product information published on the internet from multiple sources. Data in different formats such as Excel, PDF, and HTML is normalized to a common schema and can be immediately utilized as centrally managed structured data. This automates competitive research and information gathering, which traditionally required a vast amount of manual work, and provides a data foundation directly linked to business in a copyright-free manner.

Automatic Collection from Multi-Data Sources

Collects product information from e-commerce sites, manufacturer sites, catalogs, etc.

Schema Unification and Normalization

Formats structures and notation variations from each data source into a unified format.

Real-time Updates

Constantly reflects the latest market information and automatically processes update differences.

Data Generation

Reconstruct the Way it's Communicated,into Product Information that Maximizes value.

AI automatically collects product information published on the internet from multiple sources. Data in different formats such as Excel, PDF, and HTML is normalized to a common schema and converted into centrally managed structured data.
This automates competitive research and information gathering tasks that previously required manual effort and time.

Product Information Feature Tags

Automatically generates tags such as "Usage" and "Features" to enhance searchability and appeal.

Image Text Tags

Reads characters on packages and labels using OCR and converts them into structured data, such as brand, material, and capacity.

Image Feature Tags

Automatically assigns tags that extract product characteristics from photos and can be used for search and recommendations.

Product Description Generation

Generates natural sentences aligned with the brand and automatically checks for AI-specific exaggerated expressions.

Data Enrichment

Incomplete Information,Turned into "Usable Data".

AI automatically collects product information published on the internet from various sources in the market. In addition to data in different formats such as Excel, PDF, and HTML, it can also ingest information from standard specifications like GS1 and complex external services.
AI normalizes it to a common schema and further assigns attributes from Lazuli DB, converting it into centrally managed structured data.
This automates competitive research and information gathering, which previously required a vast amount of manual work, and allows immediate utilization of valuable product data.

Automatic completion of missing information (product name, category, etc.)

AI automatically complements blanks and unclear elements within product information. It eliminates the need for manual processing due to variations in notation or omissions, ensuring consistent master data.

Automatic expansion from JAN to GS1 and collected data

Based on the JAN code, it matches and retrieves product data collected by GS1 and Lazuli, automatically expanding the master data.

Feature Labeling

Based on product information, it can also automatically determine if it meets predefined criteria or conditions and assign it as a new attribute column.

Data Estimation

Derive Clear "Classifications" From Ambiguous Expressions.

AI automatically estimates and structures information embedded in product images and descriptions (such as category, color, material). It also complements variations in product names and missing tags, redefining them as structured data. This builds a foundation that can be utilized for more advanced data applications like segment analysis and recommendations.

Automatic Estimation of Product Category

AI automatically identifies the category to which a product belongs based on its name, description, image, etc.

Estimation of Attributes such as Product Color and Material

Based on image analysis and descriptive information, AI automatically classifies and extracts attributes such as color, material, and size. It also learns subjective expressions, enabling classification closer to the user's perspective.

Feature Estimation

Based on the features included in the product information, AI automatically estimates whether it matches predefined conditions such as "Food for Specified Health Uses" or "Gluten-Free" and assigns labels.

Data Collection

From "Collecting" to "Usable" Data.

AI automatically collects product information published on the internet from multiple sources. Data in different formats such as Excel, PDF, and HTML is normalized to a common schema and can be immediately utilized as centrally managed structured data. This automates competitive research and information gathering, which traditionally required a vast amount of manual work, and provides a data foundation directly linked to business in a copyright-free manner.

Automatic Collection from Multi-Data Sources

Collects product information from e-commerce sites, manufacturer sites, catalogs, etc.

Schema Unification and Normalization

Formats structures and notation variations from each data source into a unified format.

Real-time Updates

Constantly reflects the latest market information and automatically processes update differences.

Please Feel Free to Ask Any Questions or Consult with Us.

Have challenges managing your product data? We’re here to help you unlock its full potential.

Please Feel Free to Ask Any Questions or Consult with Us.

Have challenges managing your product data? We’re here to help you unlock its full potential.

Please Feel Free to Ask Any Questions or Consult with Us.

Have challenges managing your product data? We’re here to help you unlock its full potential.

よくあるご質問

Q

LazuliのAIアプリケーションとは何ですか?

Q

商品説明文やタグの自動生成は正確ですか?

Q

ハルシネーションをどのように防いでいますか?

Q

AIアプリケーションはどのような業務に活用できますか?

Q

導入後にどのような成果が期待できますか?

Lazuli PDPの真価は、組み合わせてこそ発揮されます。

各機能が連携し合うことで、単体利用では得られないデータ活用力を実現します。

A New Generation of PIM

Free Yourself from Complex Data Management

Eliminate Quality Inconsistencies from Manual Work

Automate Tedious Tasks

Automatically Tagged Images. Searchable with AI.

Automatically Turn Images into Valuable Data

Find the Right Image Just by Describing It

Accelerate Cross-Department Collaboration

AI Applications

Give Product Data the Power of Thought.

Data Generation

Data Enrichment

Data Estimation

Data Collection

AI Agent

Put AI at Your Team’s Right Hand.

POS/OMO Analysis Agent

Inventory Management Agent

Item Recommender Agent

Please Feel Free to Ask Any Questions or Consult with Us.

Have challenges managing your product data? We’re here to help you unlock its full potential.

AIアプリケーションは、単なる格納システムを超えて、

データに“意味”と“使い道”を与えます。

LazuliのAIはPIMやDAMに蓄積された商品情報を読み解き、タグ付けや分類、文章生成などを通じて“業務で使える知識”へ変換。

マーケティング、分析、商品開発、営業支援など、あらゆる業務を加速させます。

Lazuli’s AI PIM converts scattered, inconsistent product information into AI-ready formats, building a next-generation data foundation that drives business growth. By managing product data through AI PIM, companies can enable end-to-end AI processing and analytics rooted in their product information.

AI-Ready Product

Data That Accelerates

Your Business

AI-Ready Product Data That Accelerates Your Business

AIアプリケーションは、単なる格納システムを超えて、データに“意味”と“使い道”を与えます。

LazuliのAIはPIMやDAMに蓄積された商品情報を読み解き、タグ付けや分類、文章生成などを通じて“業務で使える知識”へ変換。

マーケティング、分析、商品開発、営業支援など、あらゆる業務を加速させます。

EN

LunarFlex Pro

Relax Fit Crew Neck T-Shirt

Haven Luxe Modular Sofa

Veloria Red Reserve

Sour Cream & Onion Potato Chips

Generate

Description

Advanced Settings

A crew neck T-shirt made from soft, breathable organic cotton. Designed with a relaxed fit for all-day comfort, it’s perfect for casual styles. Durable against washing, this tee is ideal for everyday use.

Quality

Place of Origin

Materials

Target Users

Generate

Product Name

Brand Name

JAN Code

Product Size

Products

AI Applications

Give Product Data the Power of Thought.

Lazuli's AI automatically analyzes and converts text, images, and unstructured data into data that can be immediately used in PIM, DAM, and external operations.

Products

AI Applications

Give Product Data the Power of Thought.

Lazuli's AI automatically analyzes and converts text, images, and unstructured data into data that can be immediately used in PIM, DAM, and external operations.

LunarFlex Pro

Relax Fit Crew Neck T-Shirt

Haven Luxe Modular Sofa

Veloria Red Reserve

Sour Cream & Onion Potato Chips

Generate

Description

Advanced Settings

A crew neck T-shirt made from soft, breathable organic cotton. Designed with a relaxed fit for all-day comfort, it’s perfect for casual styles. Durable against washing, this tee is ideal for everyday use.

Quality

Place of Origin

Materials

Target Users

Generate

Product Name

Brand Name

JAN Code

Product Size

Products

AI Applications

Give Product Data the Power of Thought.

Lazuli's AI automatically analyzes and converts text, images, and unstructured data into data that can be immediately used in PIM, DAM, and external operations.

LunarFlex Pro

Relax Fit Crew Neck T-Shirt

Haven Luxe Modular Sofa

Veloria Red Reserve

Sour Cream & Onion Potato Chips

Generate

Description

Advanced Settings

A crew neck T-shirt made from soft, breathable organic cotton. Designed with a relaxed fit for all-day comfort, it’s perfect for casual styles. Durable against washing, this tee is ideal for everyday use.

Quality

Place of Origin

Materials

Target Users

Generate

Product Name

Brand Name

JAN Code

Product Size

LunarFlex Pro

Relax Fit Crew Neck T-Shirt

Haven Luxe Modular Sofa

Veloria Red Reserve

Sour Cream & Onion Potato Chips

Generate

Description

Advanced Settings

A crew neck T-shirt made from soft, breathable organic cotton. Designed with a relaxed fit for all-day comfort, it’s perfect for casual styles. Durable against washing, this tee is ideal for everyday use.

Quality

Place of Origin

Materials

Target Users

Generate

Product Name

Brand Name

JAN Code

Product Size

Products

AI Applications

Give Product Data the Power of Thought.

Lazuli's AI automatically analyzes and converts text, images, and unstructured data into data that can be immediately used in PIM, DAM, and external operations.