Our tools already support many clients developing successful Artificial intelligence automation for numerous micro-verticals such as Financials, Trustee Reports, or KYC documents.
If you are looking for a proven partner to support you leverage Deep Learning to extract actionable information from unstructured documents of any kind, we are up for the challenge.
More data beats better models. Compared to traditional Machine Learning models, prediction quality of Deep Learning models does not plateau with more data.
Deep Learning needs annotated data, which, together with the data collection, usually is about 80% of the effort! After all, Deep Learning is 80% data management and 20% complaining about the data.
Developing Deep Learning-based AI models to automate narrow tasks requires the combination of several models specializing in certain jobs, such as OCR or NLP.
One significant difference between statistics and Machine Learning is that the former is optimized to be correct on average, while the latter is optimized for business efficiency.
Likely, initially, human-machine integration will be necessary until full automation of the micro-vertical is achieved.
Early integration of AI-based models in the workflow is particularly important to monitor results and collect data on manual interference to further train the models.
Through the seamless integration of all steps in the Deep Learning workflow, innolytiq transforms it into an accelerating loop, enabling unparalleled speed of AI-based solution development.
We have just released the gamified mobile application as an early preview. Rather than dedicating full-time employees to solely annotation, this mobile application allows employees to label data alongside their actual work during an unproductive time, such as commutes or waiting for an order.
Using employees solves the problem of domain knowledge necessity as well as data privacy. The gamification approach allows for working on this task without dedicated full-time employees, hence matching the nature of being a one-off task.
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General manager: Philipp Dainese