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AI Product Matching Software Built for Large-Scale and Complex Catalogs

Real estate, construction, manufacturing, energy, and industrial distribution organizations rely on accurate matching of products, materials, and assets across fragmented datasets. Yet most systems struggle with inconsistent descriptions, missing attributes, and supplier-level variation. Scry AI brings AI product matching into a structured, scalable workflow that improves discovery, comparison, and decision-making.

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Reality of Product Matching in Industrial & Large-Scale Catalogs

Product catalogs in construction, manufacturing, and energy run deep, often spanning millions of SKUs described in inconsistent formats, varied abbreviations, and incomplete attributes across systems and suppliers.

In day-to-day workflows like procurement, customer service, and reconciliation, identifying equivalent or duplicate products is not optional. As data volumes grow, relying on manual matching, classification, and standardization introduces errors, slows operations, and creates inconsistencies that affect procurement efficiency, inventory accuracy, and overall operational reliability.

How Product Matching Still Works Today

Product matching still depend on navigating fragmented catalogs, inconsistent descriptions, and manual validation steps. This approach increases effort, slows matching accuracy, and limits scalability across large datasets.

1
Manual lookup
Teams rely on keyword searches to find similar products, often missing relevant matches due to inconsistent naming.
2
Complex specification comparison
Attributes such as size, location, or material specs are cross-checked manually, increasing effort and variability.
3
Time taking part number or listing matching
Matching is done based on IDs or SKUs without validating contextual similarity.
4
Manual classification
Products are categorized based on individual judgment, leading to inconsistency.
5
Duplicate detection gaps
Basic search methods fail to identify near-duplicate listings or functionally similar items.
6
Manual description creation and code assignment
Short and long descriptions are written manually, slowing onboarding. Material or asset codes are assigned through manual validation, limiting scalability.

Scry AI’s AI-Based Product Matching Solution

Scry AI delivers an AI-Based Product Matching Solution designed to handle large-scale, real-world datasets with precision. Built on Collatio® and Auriga®, it introduces intelligence across matching, classification, and enrichment workflows. The solution brings structure, context, and automation into AI product matching in real estate and related domains.

  • Context-aware matching

    Understands unstructured product descriptions, SKUs, and attributes to identify true equivalence beyond keywords.

  • Semantic similarity and ranking

    Delivers top match recommendations based on functional and contextual similarity, not just text overlap.

  • Intelligent similarity ranking

    Ranks products based on similarity and surfaces top match recommendations.

  • Advanced duplicate detection

    Uses multi-parameter scoring and fuzzy logic to identify near-duplicate products.

  • Automated classification

    Assigns categories such as product type, material class, or hierarchy with consistency.

  • Automated Description & Code Generation

    Produces standardized descriptions and validated material/asset codes for improved searchability and consistency.

  • Smart Data Enhancement

    Identifies gaps, enriches specs from external sources, and scales thousands of records in parallel.

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Turn Product Matching into a Scalable, Intelligent Workflow

See how AI product matching in real estate can improve accuracy, speed, and consistency across your data workflows.

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FAQs

What is AI product matching in real estate?

It is the use of AI to identify equivalent or similar products, materials, or listings by understanding descriptions, attributes, and context instead of relying only on keywords or IDs.

Traditional systems depend on exact matches or rule-based logic. Product matching AI uses semantic understanding, similarity scoring, and automated classification to deliver more accurate results.

Yes. The solution is designed to work with unstructured and partially available data, while flagging gaps and enriching records where possible.

It applies across real estate, construction, manufacturing, mining, oil and gas, industrial distribution, retail, and e-commerce.

Yes. The system is designed to support large-scale catalogs and enables bulk processing with scalable performance.