YOUR DATA. OUR SCIENTISTS. YOUR INTELLECTUAL PROPERTY.
OUR EXPERT DATA SCIENTISTS ARE ENTHUSIASTIC TO SUPPORT YOU IN YOUR DEEP LEARNING AUTOMATION JOURNEY.
Using the most advanced AI platform - innolytiq - we develop models on a tiny and redacted or falsified dataset. Then, these models are trained on the full dataset at the client site without us having access to the data. With this mechanism, we can guarantee 100% data privacy uniquely combined with fully customized, hence much more accurate, models. Even our multi-purpose models, such as Table Detection, which already perform at unmatched accuracy, can be further trained at the client site. Additional training on client-specific data allows for even higher accuracy to the degree of AI-only full automation.
DEEP LEARNING NATIVE
The latest advances in practical Artificial Intelligence are all applications of Deep Learning to specific problems. However, while Deep Learning outperforms traditional Machine Learning methods significantly, it also requires very different management. Traditional Machine Learning relies on feature engineering from domain experts and generally includes a rule-based system. In contrast, Deep Learning predicts what a human would do based on previously annotated, i.e. labelled data. This presents a fundamental shift in Artificial Intelligence and requires a distinctly different way of management and skillset. Data scientists at cognaize are pure Deep Learning specialists and understand the significant change and its nuances viscerally. We have a very successful track record of providing proper Deep Learning solutions with an unparalleled degree of automation.
SPEED THROUGH REUSABLE MODELS
Enterprise Deep Learning solutions regularly combine several models into a pipeline to produce final automation. Cognaize already has an extensive set of horizontally scalable, multi-purpose Deep Learning models, readily integrable to provide faster and more reliable results.
For example, our Table Detection model, trained on over 1.5 mln financial documents, extracts tables and hierarchies within tables with exceptional accuracy compared to the competition. The same is true for our page structure model, predicting the structure of a document through marking headers, subheaders, text blocks, or lists. Equally significant is the key-value pair model that predicts pairs such as the label invoice number and the respective number independent of the context. Moreover, our Named Entity Recognizer predicts organizations, locations, ratings, and many other entities with unparalleled precision.