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