Modern information extraction and natural language processing (NLP) techniques are able to extract correct information from unstructured documents, even if it contains some name variations or misspelled words. This can reduce the cost and time of traditional brief analysis and legal research. Information extraction can also aid in document generation, which includes drafting of legal documents.
Managing a large amount of contractual obligations can be challenging. Machine learning (ML) methods can extract legal obligations, parties and types of clauses across contracts, leading to a more efficient use of human resources and fewer mistakes. Contract review can also profit from ML and NLP techniques, which can aid attorneys to identify issues and risks on contracts and automate repetitive tasks.
With the amount of data available, legal research can be expensive and time-consuming. Machine learning can facilitate work with large amounts of data by identifying relevant documents. It is also possible to create methods to learn patterns, extract needed information and gain useful insights from legal documents.
We are a software company specialized in Algorithms and Machine Learning (ML) and most of our 30+ employees have a scientific background in Mathematics and Physics. We follow the developments in ML research and have multiple years of experience bringing this knowledge to customers and into production.
No Machine Learning task is alike. The data that is available and the requirements always differ and might not be captured by off-the-shelf software. From us, you get an individual solution. You own the code, the data and we integrate it into your current software environment.
We offer initial proof of concepts and the complete development of production software. Due to the experience and background of our team we can as well offer larger research projects.
In our Tech Lunch, we will give you 45 minutes of information on a ML topic of your choice.
In our ML Expert talk you will get 1 hour consultation & discussion about your specific use case.
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