Natural Language Processing deals with how to recognize patterns in natural, unstructured text. Think of structured text as data in a database or excel table, for instance a register of names. By unstructured information we mean text in emails, documents, manuals etc. The term natural highlights that the text data has been generated by a human for another human.
Currently the process is cost-intensive and/or a faster decision creates substantial value
A (trained) human could make a good decision mainly based on text
There is enough data available (as a rule of thumb: 500 - 10.000 documents. This, of course, is highly dependent on the use-case)
The last years have seen tremendous improvements with regards to the quality of pattern recognition in unstructured data. The reason for this is next to hardware improvements mainly a group of algorithms, which go by the name of neural nets or deep learning. A key feature of these approaches is that given enough training data, they form their own set of rules in order to achieve a certain goal. This way millions of implicit rules may be defined to successfully recognize even rather complex patterns. In our experience, only by combining know how of internal operations with natural language processing expertise, projects can be framed well. Feel free to approach us with questions, especially whether we deem your project to be technically feasible.
Together we discuss your process automation projects along three different dimensions: cost savings, strategic value and technical feasibility. After settling for a specific project, we put special emphasis on the needs of the end users.
We are an experienced team of machine learners. Our algorithms find complicated patterns in unstructured, mostly visual and text data. Once detected, these patterns are the basis for the automation of the underlying process.
We make a point of integrating our customers in the project's code repository as well as in weekly progress meetings. Agility, clean code and a modular program structure help us to deliver easy-to-maintain software, that simply works.
Ewelina Fiebig
Machine Learning Scientist
Fabian Gringel
Machine Learning Scientist
Fabian Gringel
Machine Learning Scientist
Mattes Mollenhauer (PhD)
Machine Learning Scientist
Ewelina Fiebig
Machine Learning Scientist
Fabian Gringel
Machine Learning Scientist
Konrad Schultka (PhD)
Machine Learning Scientist
Jona Welsch
Machine Learning Project Lead
Konrad Mundinger
