Legal professionals face many challenges in today’s document review landscape. Client data is only growing (especially with different kinds of emerging data types) and tackling large and complex datasets is nearly impossible with manual and linear human review. With Clustering, Everlaw’s unsupervised machine-learning solution, legal teams can easily navigate large datasets with cutting-edge AI technology that is seamlessly integrated into an all-inclusive, cloud-native platform.
Join Changie Chang, Associate Product Lead, and Tom McKechnie Ward, Customer Success Manager, to learn how Clustering can help reduce the risk of human error in document review whilst enabling to efficiently and successfully find key pieces of evidence. They will be discussing:
• Clustering’s unique capabilities
• Top use cases
• And, more!
Presenters:
Changie Chang, Associate Product Lead, Everlaw
As an Associate Product Lead at Everlaw, Changie investigates feature ideas and user workflows and works collaboratively with designers and developers to implement creative solutions. They are the Product Lead for Everlaw’s Clustering.
Tom McKechnie Ward, Customer Success Manager, Everlaw
Tom has been at Everlaw for 2.5 years. Tom specialised in machine learning while earning his BSc in Computer Science & Mathematics from Bristol University, and has leveraged his expertise to enable law firms & corporations to use the latest technology to provide clear, tangible business advantages.
