Onit Catalyst for ELM vs. Intraspexion

Onit Catalyst for ELM goes beyond conventional reporting by providing context to your spending on external legal counsel, facilitating complex decision-making while maintaining alignment with your organization’s goals. By accessing the Onit Catalyst for ELM legal business intelligence platform, you can log in below to utilize our software designed to enhance the efficiency of corporate legal departments, enabling them to make smart strategic decisions that can lead to reduced costs for outside counsel. Our advanced machine learning technology takes your data and organizes, refines, and enriches it, turning disorganized numbers into valuable information and actionable insights. Furthermore, our intuitive quarterly reporting and analysis deliver critical insights into your most important practice areas, legal firms, and ongoing cases, helping to streamline your legal operations. This holistic methodology not only allows your legal team to react effectively but also empowers them to actively contribute value to the organization. Ultimately, Onit Catalyst for ELM equips legal departments with the tools they need to thrive in a competitive environment.

The yearly financial burden linked to commercial tort litigation aimed at businesses, which includes payments for benefits, incurred losses, legal fees, and administrative costs such as document retrieval and lawyer consultations, is estimated to be approximately $160 billion, resulting in a staggering total of nearly $1.6 trillion over a ten-year period. To develop a deep learning model concentrating on the civil rights-employment category within federal courts, specifically addressing "employment discrimination," we compiled factual allegations from 400 federal court complaints, intentionally excluding any email data. This model was then launched on GPU instances in both Microsoft Azure and AWS, and it was assessed using 20,401 emails obtained from the Enron dataset, marking the model's initial exposure to email content. Each email identified as a true positive can be exported to a platform designed for internal investigations or case management, significantly increasing the model's practical applications. Additionally, by integrating a database with the user interface, users can archive these true positives, add them to the original training dataset, and re-train the model to enhance its accuracy and effectiveness. This ongoing learning process not only improves the model's performance but also ensures its continual adaptation to better recognize pertinent cases in the future, reflecting the dynamic nature of litigation and compliance in the business landscape. Overall, such advancements highlight the importance of leveraging technology in legal contexts to streamline processes and improve outcomes.