How We Improved Event Matching Accuracy with Semantic AI on AWS

Managing event matching at scale

The organization receives event listings from multiple ticket sellers, each with varying naming conventions, metadata structures, and event descriptions. Existing matching processes relied heavily on rule-based and fuzzy matching techniques, which often struggled to accurately identify corresponding events across systems.

This resulted in:

• Inconsistent event mappings across catalogs
• High levels of manual review and validation
• Duplicate or mismatched event records
• Increased operational effort to maintain data quality

The organization required a more intelligent approach capable of understanding the context and relationships between event attributes rather than relying solely on keyword similarity.

AI-driven semantic event matching on AWS

We developed a proof of concept that combines semantic AI, vector embeddings, and intelligent scoring mechanisms to improve event matching accuracy and operational efficiency.

• Semantic Event Matching Engine: Built an AI-powered event matching solution using vector embeddings and semantic search techniques to identify relationships between events beyond traditional text matching methods.
• Event Data Normalization Pipeline: Developed a preprocessing framework that standardizes event information from multiple seller feeds and internal catalogs, ensuring consistency before matching occurs.
• Assurance-Based Matching Engine: Implemented a scoring-driven similarity model that automatically identifies high-confidence matches while routing ambiguous cases for human validation.
• Scalable Vector Search Architecture: Designed a modular architecture using embeddings and vector-based search capabilities that can scale across growing event catalogs and support future AI enhancements.

Smarter event matching and better data quality

The solution delivered measurable improvements in event catalog management while reducing operational overhead.

• Improved Match Accuracy: Enabled more precise mapping between seller events and internal event records through semantic understanding of event attributes and metadata.
• Reduced Manual Intervention: Automated the majority of event matching activities, allowing teams to focus only on uncertain or complex cases requiring validation.
• Stronger Data Consistency: Improved the quality and reliability of event information across systems, reducing duplicate records and mismatches.
• Scalable Foundation for Intelligent Catalog Management: Established an AI-ready architecture that can support future enhancements such as recommendation engines, automated catalog enrichment, and intelligent marketplace operations.

zeb helps organizations apply generative AI and semantic intelligence to solve complex data matching challenges, improve operational efficiency, and enhance data quality at scale.

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