G2

Mark M.

Director Product Strategy

Small-Business (50 or fewer emp.)

9/6/2026

"Faster Entity Resolution and Smarter Matching with Senzing"

5/5

What do you like best about Senzing?

We had built our own entity resolution module and used it successfully at clients with over 100M records. But we wanted faster performance and better matching intelligence. We got both with Senzing. And their SDK made it easy to integrate into our platform. Review collected by and hosted on G2.com.

What do you dislike about Senzing?

The initial integration of Senzing was quick and painless. But there are other capabilities that you have to dig a bit deeper to understand. I think they should publish more detailed examples (documents or videos) for training on the advanced features, such as their incremental learning. This saves us so much time, and improves matching, but we didn't know how it worked initially. Review collected by and hosted on G2.com.

What problems is Senzing solving and how is that benefiting you?

Our platform creates a hypergraph from conventional data sources. Senzing then auto-populates our graph with 'possibly related' people or companies. We benefit from their core capability, matching of entities, but we also get interesting 'unusual' links between parties. A shared phone number? Shared address? Why? This improves our fraud detection and customer management capabilities. And with matching we don't just get a score, we get the reasons for the proposed match. This helps our customers confirm the match. We prepare our customer's data for use by their preferred AI agents. If we don't have a clear, deduplicated set of customers then the results from AI are suboptimal. We are bringing the data together, Senzing then ensures it is clean and ready for AI. Review collected by and hosted on G2.com.