The record of firsts and onlys in entity resolution.
Firsts & Onlys
A sixth-generation engine that covers every need: from 1M-record workloads to a hundred billion records, across real-time transactional, dynamic (just-in-time), and Spark batch, for both agentic and traditional workflows.
The Through-Line
Every release advances the same story: an engine that scales in both directions, reaches across the world’s data, and moves natively into agentic AI.
Sub-second cold start, a ~560 MB footprint, pure in-memory mode, and a composable SDK. Resolution runs serverlessly for agentic workloads and the largest datasets at the top, and on edge devices at the bottom.
Native cross-script matching between CJK (Chinese, Japanese, Korean) and English, with no transliteration. Many more languages and scripts are supported through transliteration, alongside semantic search and regionally-trained address parsing.
Senzing was first to bring entity resolution to a large language model with Conversational Entity Resolution. It followed with the first standalone MCP server for entity resolution, so agents can stand up resolution pipelines directly.
Always-On Foundations
These properties are built into the engine itself and hold across every advance on the timeline. Explore each in depth.
An embeddable library that runs inside your own stack, so no data ever flows to Senzing.
Resolves from built-in principles, with no per-dataset training or tuning.
Organizes data around entities and self-corrects as new records arrive.
Resolves in real time, one record at a time, as data streams in.
Surfaces the non-obvious connections between resolved entities.
Every match and non-match is transparent: the why, why-not, and how.
The same result no matter what order records are loaded.
Any single record can be removed and its effect cleanly, fully reversed.
Every resolved value traces back to the exact source record it came from.
Marquee moments from a fast-moving release cadence. For every point release in between, see the full release notes.
Fully serverless entity resolution with incremental adds and no full reloads. Later benchmarked at about 10 million records in about 3 hours for under $100.
Entity resolution brought to a large language model via plug-in, roughly six months after ChatGPT launched. The origin point of the agentic arc.
Automatic deterministic and non-deterministic encryption of sensitive columns, on the fly. Unique in the industry.
Free, Senzing-ready JSON snapshots (sanctions lists, corporate registries, offshore leaks, watchlists), so teams can prove value and explore third-party data in minutes, with no mapping.
Native CJK↔English matching for names and addresses with no transliteration, alongside Why Search, Record Preview, Match Key Details, Feature Traceability, and embedding-based semantic search.
A first commercial customer surpassed 10 billion input records; a public-sector deployment reached roughly 10× larger. Real-time resolution at that scale, from a single composable engine.
The free universal place identifier brought in-house to strengthen location matching, and kept open for the community.
Cold start cut to about half a second, down from several seconds, and the default for everyone. It makes the engine viable inside agentic workflows and serverless functions.
A standalone Model Context Protocol server: natural-language data mapping, SDK scaffold code, and about 30 example repositories. It never sees your data.
End-to-end agentic pipelines across Spark batch, transactional SQL, and hybrid modes, plus one-click agentic entity resolution in the Kiro IDE that cuts new-source onboarding from about 300 hours to under three.
86 million organizations, 101 million contacts, and 142 million locations in Senzing-ready JSON, with Placekey among 162 reference identifiers.
The engine reduced to about 560 MB, roughly 7× smaller, with regionally-trained address parsing and a pure in-memory mode for instant resolution over a subset. Built for OEM embedding and the edge.
New capabilities land on a steady cadence. See every release in detail, or put the engine to work on your own data.
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