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Attribute Extraction and Normalization

Decode Product Attributes With Next-Gen Multimodal AI

Extract, tag, and normalize product attributes with precision using AI-powered multimodal analysis

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Why Product Attributes Are Hard to Decode

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Attribute Diversity

From fabric type in fashion to technical specs in electronics every industry has unique attributes

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Inconsistent and Unstructured Data

Categories, brands, and retailers define and categorize attributes differently

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Ambiguous and Missing Attributes

Product details like material type, dimensions, or certifications are often absent or inconsistently labeled

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Image and Text Misalignment

Product descriptions and images often contain conflicting or incomplete information disrupting accurate attribute extraction

Bring Order to Product Data With Domain-Trained AI

Multimodal Attribute Extraction, Tailored for Every Industry

Product attributes vary by industry, from ingredients in grocery to screen size in electronics. Our AI-driven solution accurately extracts and standardizes these attributes at scale.

LLM-Based Normalization for Accurate Data Mapping

Align brand-specific terms with industry standards using AI-powered contextual understanding. From size normalization to variant mapping, our solution ensures clean, structured data for better insights.

LLMs for Enhanced Semantic Understanding

Unlock deeper insights with AI-driven semantic analysis. Our LLMs extract key attributes, resolve ambiguities, and map products accurately—no matter how they’re described.

Driving Better Decisions with Accurate Product Attribute Extraction

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Better Competitive Intelligence

Accurate attribute-based product matching enables precise benchmarking and market positioning.

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Optimized Catalog Management

Automated, scalable attribute tagging reduces manual effort and ensures accuracy and consistency.

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Assortment Analytics

Structured attribute data helps identify gaps, track competitor offerings, and refine product strategies.

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Brand Protection and Compliance

Detecting certification marks, logos, and counterfeit indicators help safeguard brand integrity.

Frequently Asked Questions

Why is accurate product attribute extraction important for retailers and brands?

Accurate attribute extraction ensures that key product details—like size, color, flavor, quantity, and specifications—are consistently and correctly captured across channels. This forms the backbone of reliable product matching, powering competitive benchmarking, price tracking, assortment analysis, and content optimization. Without clean and consistent attributes, comparisons across marketplaces become unreliable, leading to poor search relevance, inconsistent customer experiences, and lost conversions.

How does DataWeave ensure consistency in attribute data across different marketplaces?

DataWeave uses large language model (LLM)-based normalization and AI-driven rules to standardize product attributes across platforms. It intelligently maps synonyms (e.g., “XL” vs. “Extra Large”), resolves brand-specific naming conventions, and aligns variations in units, pack sizes, and descriptors. This ensures consistent, structured data—essential for accurate comparison, content optimization, and analytics across diverse retailer ecosystems.

How does DataWeave’s multimodal AI improve attribute tagging and normalization?

DataWeave’s multimodal AI combines the power of both text and image analysis to enhance attribute tagging and normalization. By processing product descriptions, titles, and images, our AI ensures that product attributes are accurately extracted, standardized, and aligned across different retailer sites. This approach ensures consistent and reliable product data, improving product matching, content accuracy, and overall eCommerce performance across various platforms.

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