Senzing

Senzing is entity resolution — deciding which records refer to the same real-world person or organization — delivered as an embeddable SDK. This connector is Claude's authoritative, tool-first source for building and evaluating entity resolution: it grounds every answer in Senzing's own documentation and indexed code rather than model training data, which is frequently wrong about Senzing. Because it carries the full runbook, you don't drive it one step at a time. Hand Claude a few unmapped data files and describe what you want, and it can map your sources, deploy and load Senzing, resolve and analyze the data, and build an interactive explorer over the results — turning what is normally a multi-week integration into an afternoon, with no entity-resolution background required. It also covers the pieces on their own: SDK code in Python, Java, and C# (official) plus Rust and TypeScript/Node.js (community), installation and deployment, error troubleshooting, V3→V4 migration, and the business case. You can ask Claude to: "Here are three customer files from different systems — build me a demo that finds the same people across them and lets me explore the matches." "Map this data to Senzing and show me what resolves." "Generate a pipeline to load and resolve these records, then measure the match quality." "How would entity resolution help with my problem, and what would it cost?" It works entirely from pre-fetched documentation: no live Senzing instance, no authentication, no user data or PII sent to Senzing, and all data mapping runs locally on your machine. ***Get the best experience with Claude Code and/or mcp.senzing.com allowlisted in Chat/CoWork.