Three years ago this project was estimated at $35,000 and a month of developer time just to reach a working prototype of our customer intelligence system. With the Senzing MCP server and Claude as a development partner, I built a fully functioning system in 16 hours by myself. Creating a transactional data store from five customer-facing platforms, piped into Senzing, with a dashboard and accurate conversational intelligence on top. ~90,000 records resolved into ~30,000 customer entities. Household detection via shared address. Cross-platform name variant matching . Operator-driven force-resolve for the edge cases the engine couldn't auto-resolve on its own. The architecture, Senzing as the Entity resolution layer, conversational queries via Claude MCP, was the vision from day one, informed by years of studying the problem and the data. What changed was access: the combination of Senzing's MCP server and Claude as a development partner made it executable by one person in days rather than a small team over 4 or more weeks. This is the future of how intelligent systems get built.

Senzing can work at a scale and accuracy like no other equivalent solution we have identified so far. The team behind Senzing is also good fun to work with.

I really appreciate that Senzing is very easy to install. It's not just about doing analysis or figuring out data. If you ask why it did something, Senzing explains that very easily. I like that it's not a record-to-record matching, but entity-to-entity matching, which is very complex because with more data, you enrich one particular entity's data and Senzing does that out of the box. For example, Abdullah can be written in 54 different ways, and that's where Senzing really, really helps because you can't code this manually.