Most first scans surface 30 to 40% entity fragmentation. MatchLogic's entity resolution software clusters scattered records into unified profiles with pre-trained AI and shows the reason behind every link. Get a demo on data that looks like yours.
Records matched
Match accuracy
Time to first profile
.png){kind=small}
.png){kind=small}
No black-box matching. No months of setup. Just unified profiles with full visibility into why every record was resolved.
When Mike Rogers links to Michael Rogers you see name similarity, address match, and phone correlation scores for the pair. Adjust any threshold.
.avif)
MatchLogic resolves entities accurately out of the box, with no model training, no tuning, and no resolution experts required. Deterministic, explainable, same input, same answer.
.avif)
MatchLogic installs inside your own network. Nothing uploads and nothing leaves, so you can evaluate entity resolution on real production data safely.
.avif)
Trigger resolution runs through the REST API, or connect any LLM through the MCP server to run workflows end to end. The model never accesses your records.
Everything the enterprise version offers, shown live on your demo.
Entity clustering that works out of the box, no labeled data required.
Cleansing and match rules proposed from your data, approved by you.
Ask questions about resolution results in plain language.
Field-by-field reasoning behind every AI decision.
Entity IDs by CSV, REST API, or warehouse push.
The first time we ran it against a year of claims, it drew a map we had never been able to see. One address tied together eleven claims we had treated as strangers. We did not have to take the system's word for it, because it showed the reason behind every line it drew.
Director of the Special Investigations Unit, Acuity Insurance
A specialist runs the full product live on data like yours: profiling, resolution rules, entity clusters with confidence scores, and unified profile export. Nothing is staged, and you can bring your own scenario to steer it.
Fill out the form and pick a time that works. The demo runs about 30 minutes on your data scenario, with time for questions on your systems and compliance requirements. No commitment afterward.
Yes. MatchLogic is a full on-premise install, so records never leave your network. MatchSense is non-generative and runs entirely inside your environment, including air-gapped, and an LLM connected through the MCP server operates workflows without ever accessing your data.
Most companies discover 30 to 40% entity fragmentation they never knew existed. Resolution catches nicknames hiding as formal names, typos creating false duplicates, and company abbreviations splitting single entities. These variations cost real money in duplicate processes.
No. MatchLogic is pre-trained: it resolves entities accurately out of the box with no model training, no tuning, and no labeled data. Setup assistance is also AI-driven, with cleansing and match rules proposed from your data for your approval.
The Docker-based install runs on any infrastructure in under 5 minutes, entirely inside your own network. You can connect SQL Server, PostgreSQL, MySQL, Oracle, or flat files, or load a CSV directly.
.svg)
