Anzyz

Second generation AI

Language is enormously diverse and nuanced, and can be particularly complex for a machine to understand across industry verticals. The exact word order can carry critical information, raising the need for high-precision technology that captures critical nuances. Consequently, standard machine learning approaches struggle immensely as they largely rely on learning representations of individual words. Anzyz’ AI driven Natural Language Processing (NLP) technology is built from the ground up on fundamentally different principles than existing NLP solutions. We call our technology Corpus Cube Linguistics, CCL™ in short.

No manual tagging

Train on any set of raw (untagged) data at limited resources and with limited computational effort.

No loss of data

Complex probabilistic network of relations is built with no information thrown away.

Explainable

The AI is explainable and contains information about decision-making.

Precision

Best-in-class precision even when trained on limited data sets – superior practical value vs existing solutions in a broad range of use cases.

Access data faster

Anzyz CCL™ solution enables faster access to any type of text data in any format or language. Our solution analyzes and understands Big Data “from the inside-out”, and makes it possible to integrate unstructured and structured data, across information sources. Spend less time searching for data and access all information in one knowledge base.

Contextual meaning

Anzyz CCL™ understands interdependencies and connections in words, sentences and context – wholly based on self-learning from raw data set, the solution is thereby able to understand expressions and characteristics, as well as terminology, misspellings and abbreviations, in any natural written language.

No manual tagging

Avoid tagging single words (so called manually-based rules or indexing) and spending huge amounts of time to manually go through Big Data. Manually-based indexing systems have proven to be very difficult and highly time consuming. In contrast, with our unique unsupervised machine learning we deliver an automated solution which requires significantly fewer resources.