Blog Topics: Brainspace Discovery, News
Brainspace named to National Law Journal’s 2018 AI Leaders
By David Copps, Co-Founder and CEO.
Brainspace was recently honored by The National Law Journal as a top artificial intelligence company in the legal industry.
The full-page profile in the publication’s inaugural list of Legal AI Leaders recognizes Brainspace for its unique approach to e-discovery. Brainspace combines cutting-edge machine learning with interactive visualizations, allowing legal investigators to quickly find the answers hidden within massive document sets.
The article also looks at the future of AI in legal investigations, where Brainspace is working on proactively analyzing new documents in real time and being able to predict or even prevent problems before they happen.
Below is the full National Law Journal article.
Click here to view the issue.
Brainspace is first and foremost a technology company. It focuses on the field of digital investigations through the combination of machine learning technology and interactive data visualizations. Its solutions utilize the patented Brainspace platform for text analytics, e-discovery, digital investigations and defense intelligence. Customers include Fortune 500 companies, consulting firms, legal service providers and government agencies. In September, the company introduced Brainspace 6, which is designed to learn dynamically without the use of lexicons or taxonomies. This provides users with a suite of interactive visualizations and search tools to reveal the story within data. Massively scalable and multilingual, Brainspace 6 is equipped to handle critical investigations of any size in any language. “Brainspace can learn from and process more than 300 languages. It’s also scalable and can learn from unstructured case data. It can learn dynamically from information on the fly.” The system brings in millions of case documents and can ingest and read them very quickly. “It reads one million documents in 45 minutes. Then, it can use that brain to go back into the data and find what users are looking for. We’ve brought the idea of augmented intelligence to the e-discovery space. There’s too much data to learn from, so we have to augment the intelligence of a practitioner to radically improve productivity.”
Brainspace has taken a different approach to this. It uses a “Cluster Wheel” concept, which clusters documents based on conceptual similarity. Users can navigate the clusters like a map, quickly identifying neighborhoods of related documents rather than looking at one document at a time. “Most e-discovery systems look like an overlay on the database. But we use dynamic visualization for people to interact with the machine learning. Think about predictive coding: When someone uses technology like ours, they are trying to find the best documents as fast as they can, so they can touch and code fewer documents. By capturing the learning from a group of exemplary documents, it can connect to others to bring back all the documents that have a similar fingerprint. We’ve invented some techniques that allow people to visualize data in a way that’s never been done before.” This lets users navigate the data set to rapidly find answers. “When a lot of documents are involved, users want to quickly understand what’s been said. We can find that very, very quickly.”
Brainspace’s founders have been working together for more than two decades to solve challenging problems in semantic search and machine learning. In 2007, after selling their company Engenium, Copps and Chris Rohde set out to create a new machine learning platform with an emphasis on interactive visualizations and massive scalability. They named their company PureDiscovery. In 2011, PureDiscovery developed its first application, PureDiscovery LegalSuite, with visualizations for the e-discovery industry. Two years later, PureDiscovery partnered with Medina Capital and received $10 million to accelerate product development and expand into enterprise and government markets. The next year, the company rebranded itself as Brainspace.
Currently, AI and machine learning are used to find hot documents. “In semantic technology, you can do a sort of cloning and use a hot email to find similar ones. In the future, we will start to see AI causing more automation and accelerating learning. We may go beyond looking for hot documents—we may start to see e-discovery heading in a more predictive and preventative direction, instead of waiting for an event and then looking backward. You may be able to prevent situations occurring almost in real time, monitor networks and look for patterns emerging before they happen. You will be able to see fraud or sexual harassment as they are occurring. Documents can be reviewed as they stream in, rather than having to deal with them reactively. We see it happening already.”
Read more on The National Law Journal