zingg.ai: Intelligent Entity Resolution for Unified Data Insights

zingg.ai is a powerful, AI-driven entity resolution platform designed to unify customer, supplier, product, and location data across diverse systems, enabling organizations to build accurate, persistent, and compliant Single Customer Views (Customer 360), Supplier 360, and Master Data Management solutions. With advanced capabilities like AI-powered matching, incremental flow, and Zingg ID—its globally unique and persistent identifier—zingg.ai ensures high accuracy and scalability, even with large, complex datasets. It supports both open-source Community and enterprise-grade versions, with native deployment on major cloud platforms including Snowflake, Databricks, BigQuery, Redshift, AWS Glue, GCP, and Fabric. The platform excels in handling dirty data through fuzzy and deterministic matching, nickname resolution, auto-transform and match, stopwords filtering, and company name normalization, all while maintaining data ownership and transparency. Its ability to scale rapidly to millions of records and reduce processing times from hours to minutes makes it ideal for modern data architectures, including composable CDPs. Trusted by global leaders such as Fortnum & Mason, Redica Systems, The Orthodox Union, and the Canadian Football League, zingg.ai delivers enterprise-grade performance with deep explainability, data stewardship, and compliance support for GDPR and CCPA. With active community engagement on GitHub, Slack, and Twitter, and resources like guides, documentation, and events, zingg.ai empowers teams to achieve data quality and integration at scale.

Key Features:

• AI for Entity Resolution with probabilistic and deterministic matching in a single flow
• Zingg ID: Globally unique and persistent identifier for entities
• Incremental Flow: Update identity graphs without full reprocessing
• Deterministic Matching: Rule-based matching for high-confidence records
• Fuzzy Matching: Handles variations and dirty data for improved accuracy
• Match on Nicknames: Enhances name matching across diverse formats
• Auto Transform and Match: Automates data normalization and matching
• Stopwords Filtering: Improves matching by ignoring common non-essential words
• Enhanced Company Name Matching: Accurate resolution of business entities
• Scaling Entity Resolution: Efficiently handles millions, billions, or larger datasets
• Verify Blocking: Reduces false positives by validating match decisions
• Knowledge Graph Integration: Enables advanced relationship mapping
• Data Standardization and Matching: Ensures consistency across systems
• Match Statistics and Explainability: Transparent insights into matching decisions
• Data Stewardship: Tools for managing and auditing entity resolution workflows
• Native Cloud Deployment: Runs natively on Databricks, Snowflake, BigQuery, Redshift, AWS Glue, GCP, and Fabric
• Cloud-Specific Features: Includes Unity Catalog integration and optimized Snowflake performance
• Open Source & Enterprise Support: Community edition available with enterprise upgrades
• Composable CDP Architecture: Enables flexible, scalable customer data platforms
• GDPR & CCPA Compliance: Supports data privacy and regulatory requirements
• Reverse ETL Integration: Outputs results to BI tools, operational systems, and OpenSearch

Pricing: zingg.ai offers a freemium model with a Community (open-source) edition and two Enterprise tiers—Zingg Enterprise Lite and Zingg Enterprise—along with special pricing for startups and non-profits. The platform provides a free trial for enterprise users to evaluate capabilities.

Conclusion: zingg.ai stands out as a highly scalable, transparent, and intelligent entity resolution solution that empowers businesses and organizations to achieve unified, accurate, and compliant data across complex systems—making it a top choice for modern data-driven enterprises.