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AI-based processing of customer data

AI-based processing of customer data

Customers and relations are the most valuable assets of any company. But at closer look the data used to identify these customers is almost always a mess. It is incomplete, out of date, not as well structured as we need, spread out over multiple “silos”, and containing a significantly amount of smaller and larger errors. These errors can almost always be fixed. It is the sheer number of records that need checking and fixing that makes for a task of Herculean proportions.

It is Frosha’s mission to solve this problem by introducing Deep Learning to this field. We train Deep Learning models to clean and structure customer data, to identify duplicates and relations, and merge and migrate databases. Our system can identify “J. Smith”, “Smith, Joan” and “Ms Smiith [sic]” as referring to the same person, even though the records reside in different databases and have completely different structure. Such a task quickly gets out of hand using traditional rule based or manual approaches.

Yuval Kantor

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Product owner and lead developer at @Frosha; PhD Spatial Economics at VU Amsterdam.

Dougal Featherstone

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Tech pioneer, change catalyst operating at senior level of small to medium sized firms. Driven by creating inspired teams that use tech to redefine industries.

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