Amazon Q & Senzing AI: One Click Entity Resolution For Smarter Data Matching

Description 

Entity resolution supports almost every modern data system from AML compliance and KYC to fraud detection, customer data platforms, and supply chain visibility. For years, organizations have spent millions dealing with two main problems: setting up accurate data matching and preparing different data sources for resolution. 

That’s about to change. 

Join us for an interactive webinar demonstrating how data scientists and developers can use Amazon Q with Senzing to combine diverse data sources with unprecedented ease. Following a quick POC, this tech combination allows you to rapidly scale up to hundreds of millions, even billions, of records. True industrial-strength, production-ready technology.

AWS customers can now:

• Use Amazon Q to agentically map and load raw data into Senzing
• Conversationally explore the entity resolution statistics
• Use an MCP server to look up entities and ask for proof (explanations why)

Understanding the Challenge 

Discover why figuring out who is who in your data and who is related to whom has been one of the most difficult problems in data management, and how the right technology changes everything. 

Experience how purpose-built AI for entity resolution delivers consistent, explainable, relationship-aware entity resolution with 99% accuracy, routinely outperforming humans. See how it runs entirely within your AWS environment, arrives ready to use with no tuning or training, and requires minimal data preparation. 

This live demonstration shows how quickly you can prepare and entity resolve a data source. Watch as Amazon Q guides you through using GenAI to create Python code that converts source data to Senzing-ready JSON, submitting and processing that JSON through Senzing, and analyzing the results. You'll gain a new understanding of how accessible industrial-grade entity resolution has become. 

Hear directly from an organization already transforming their data operations with this approach, experiencing the dramatic reduction in operating costs over their systems' lifespans.