FASTEST ENTERPRISE-GRADE AI CENTRICITY.
OUR UNMATCHED AI PLATFORM SUPPORTS YOU IN ACHIEVING TRUE DEEP LEARNING AUTOMATION EXCEPTIONALLY EASY.
Deep Learning typically consists of six steps - Data Collection, Data Annotation (or Labelling), Model Development, Model Verification, Integration, and Monitoring. While each of these steps has its difficulties, data collection and data annotation are the most time consuming and expensive tasks. Therefore, rather than looking at these tasks as a separate effort, integrating annotation into the Extraction, Transformation, and Loading (ETL) processes delivers much faster automation results. Such an AI-centric approach combines the strengths of Deep Learning with human understanding delivering superior data quality at unparalleled processing times. With innolytiq, we are presenting the most advanced enterprise-ready AI-centric platform.
Looking at Deep Learning as a process rather than a building block is essential for success. Successful AI companies like Deep Mind or Tesla outperform competition based on this differentiation. Innolytiq automates three of the six Deep Learning steps entirely. Model verification, integration, Monitoring are fully automated and need no attention when using innolytiq. Additionally, Data Annotation is significantly expedited through the intuitive application in combination with machine-assisted annotation. Finally, Model Development is heavily supported with the suite of horizontally scalable base models. As a result, clients transform their linear Deep Learning process into a hyperloop saving additional valuable time with each iteration.
Innolytiq comes with a whole python package that allows easy access to training data and seamless integration of existing client models. Paired with the cognaize's horizontally scalable models - Table Detection model, Page Structure model, Key-Value Pair Detection model, and Named Entity Recognizer, innolytiq delivers a complete low code environment for fast enterprise-grade AI development.
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.
A TRUE SCIENCE PLATFORM
Following a scientific process is vital in order to achieve fast, reliable, and accurate results. Therefore, model development starts with a hypothesis tested on data.
Using innolytiq, all such experiments are inherently reproducible with immutable data and code automatically stored. Further, innolytiq lets you visualize, search for, compare, and download run artifacts and metadata for analysis in other tools.
Moreover, the model evaluation must be driven by business needs. With innolytiq, the evaluation of models is centralized and executed immutably. Besides evaluating model performance on training, validation and test datasets, innolytiq automatically assesses the performance on real-life usage by documenting all changes and reporting the performance on each document.
The automatically gathered information can be quickly used to determine the subsequent best actions, resulting in a genuinely scientific approach to automation.