Get the new libpostal data model from Senzing. It's more accurate, up to date and has broader coverage than the 2016 model.
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Jeff Jonas blogs about an article Ben Lorica wrote in his Gradient Flow newsletter about the unique technology of Senzing and potential inspiration for others pursuing the next generation of AI/ML.
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Learn why libpostal users should try entity resolution to help make sense of their address data and people/organization connections.
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Thinking of building your own entity resolution technology? Before you do, make sure you know what you're in for. Learn about 8 key capabilities needed for modern entity resolution.
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Large Language Models (LLMs) and generative AI are fundamentally changing how people interact with computers. Everything. Everywhere. Even entity resolution. Today, we are announcing another first: Conversational AI for Entity Resolution™.
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Enterprises that want to add entity resolution to their applications or services often consider whether to build it or buy it. In this blog we discuss both options and explain why it building doesn’t make sense anymore.
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Humans are producing a tsunami of data. We need smarter tools like entity resolution. We need to connect scattered, disparate big data to reveal new and important discoveries.
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Learn more about how understanding exactly why an entity resolution match was or wasn't made helps build confidence in your data and business decisions.
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Entity resolution often takes too long. At Senzing, we’ve focused on making entity resolution software fast and easy. Watch the first video in our new video series.
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Learn 3 quick ways to install and explore Senzing. Get started and load data in minutes to our desktop tool or API software.
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Check out how we've improved performance, improved infrastructure requirements, and reduced total cost of ownership (TCO) to the Senzing API.
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There are seven key capabilities, each with their own development challenges, that ISVs should be aware of when wanting to build entity resolution models into their offerings.
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