Luminance is applying cutting-edge machine learning to due diligence for the very first time. All documents in a data room are compared and contrasted while Luminance searches for patterns and anomalies. It understands language by context and content, not just by highlighting certain words.
It can do so because the technology is built on a wide array of contextual legal knowledge and understanding. Legal due diligence experts have trained Luminance on thousands of documents and contract clauses. But, like a human, it is still able to learn, so that it doesn’t need to be continually instructed on what to look for.
Luminance has not been designed to replace lawyers. Its purpose is much bigger than that – it’s going to fundamentally change the way we approach big data.
A unique approach
Other document review products on the market rely on keyword search. This approach is both limited and outdated, as the user must manually enter search terms. This is time consuming and doesn’t spot risks that the user isn’t aware they should be searching for: the 'unknown unknowns'.
What’s unique about Luminance is the recognition that simple keyword search and clause identification are not enough. Luminance has been trained by legal experts to accurately identify and label documents, clauses, people, places and language. By pairing machine learning algorithms and advanced mathematical techniques with deep domain expertise, Luminance finds the clauses and documents within the data room that deviate from the norm. This allows lawyers to instantly see what needs attention.
Simple to implement
Luminance is software and can either be installed directly into your existing systems as a physical appliance, or accessed securely via the cloud. Once the contents of the data room have been uploaded, Luminance’s machine learning algorithms read and analyse the documents to identify clauses, extract data and detect anomalies. Then you can simply log on to get an immediate overview of the entire data room.
What is machine learning?
Machine learning is a closely related concept to artificial intelligence. Recent advances in mathematics have created sophisticated algorithms that allow a machine to learn and teach itself. Machine learning systems, once trained, can be left to their own devices. In fact, the more data they analyse, the more effective they become.
These algorithms are able to detect patterns in language and use probabilistic mathematics to understand text and make predictions. Once a large enough set of data has been ingested, the machine can then detect anomalies – points that differ from the norm. This allows computers to complete much of the complex, yet repetitive, tasks that, when done by humans, are slower and more prone to error.