MatchSense runs on a purpose-built AI engine for entity resolution. The AI links every record that points to the same real-world person or organization, even when names, formats, and identifiers do not match, and it builds one golden record you can trust. It arrives pre-trained, so it resolves accurately from the first run, with no rules to build and no model to train.
Most AI matching needs weeks of labeled data before anyone can trust it. This engine is pre-tuned, so it performs from the first run, with no setup project and no specialist hires.
When two records resolve to one entity, you see which attributes matched and what drove the call. Nothing has to be taken on faith, which is exactly what audit and compliance teams want.
The engine resolves entities; it does not write text or run a language model. It invents nothing, and identical inputs always return identical results.
Each customer works with a named account manager and a product specialist, with training included. A hard dataset gets you a person who has solved that problem before.
The MatchSense AI engine runs and learns inside your environment, so no records are sent to a third party at any point. That makes both deployment options viable for regulated data.
Install on your infrastructure. Team access with multiple user licenses. Schedule recurring pipeline runs with the built-in Workflow Scheduler. Automate matching to trigger when source data updates. Calendar view for managing all scheduled tasks.
RESTful API exposes every platform feature: profiling, cleansing, matching, deduplication, and merge operations. Embed directly into your data pipelines and applications. Acts as a real-time data quality firewall between your databases and data entry forms.
The same engine that builds a customer 360 also exposes fraud rings and screens identities against watchlists. It maps how entities connect across your data.
Most AI matching needs weeks of labeled data before anyone can trust it. This engine is pre-tuned, so it performs from the first run, with no setup project and no specialist hires.
When two records resolve to one entity, you see which attributes matched and what drove the call. Nothing has to be taken on faith, which is exactly what audit and compliance teams want.
The engine resolves entities; it does not write text or run a language model. It invents nothing, and identical inputs always return identical results.
Each customer works with a named account manager and a product specialist, with training included. A hard dataset gets you a person who has solved that problem before.
Every demo starts with your data. Bring a sample file and we will walk through profiling, cleansing, AI resolution, and golden record assembly live. You will see how the engine handles your specific identity challenges, with no slide decks and no hypothetical scenarios.
Schedule a DemoAI entity resolution links records that point to the same real-world person or company, even when names, formats, and identifiers differ, using a pre-trained engine rather than hand-written rules. MatchSense groups records into entities automatically and gets sharper as it processes more data.
Traditional matching depends on rules and thresholds someone sets and maintains by hand. AI entity resolution runs on a pre-trained engine that groups records on its own, learns from new data, and corrects earlier work over time. MatchSense is accurate from the first run, with no rule-building phase.
No. The engine does one job, entity resolution. It runs no language model and generates no text, so it cannot hallucinate, and identical inputs always produce identical, explainable results.
No. The engine comes pre-trained on global name, nickname, and address libraries and is accurate on the first run. There is no labeled dataset to build and no training phase to sit through.
It stays on your infrastructure. The engine runs and learns locally, and nothing is sent to an outside service, which keeps MatchSense suitable for HIPAA, GDPR, and government data.
Yes. The engine resolves the relationships between entities and watches feature statistics, so it surfaces linked entities and flags anomalies, such as one identifier shared by many records, a frequent sign of fabricated data.
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