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What is Learned Hand?

Learned Hand is an innovative online platform designed specifically for judicial case preparation, serving judges, court clerks, and staff attorneys. It streamlines the process of reviewing motion filings by extracting relevant case information, categorizing disputes and arguments, and organizing the analysis based on the type of motion, local rules, and jurisdictional guidelines. Users can upload case documents or connect to existing court case management systems, enabling them to access analyses featuring citations linked to relevant parts of the record, ask questions, and refine their work as necessary. Additionally, the platform can create initial drafts of court orders while maintaining citations, which users can edit directly in the system or export to Word for additional adjustments. Its thorough workflow includes case intake, motion assessment, source validation, and the drafting of judicial documents. In contrast to platforms aimed at law-firm advocacy, Learned Hand focuses on judicial and court-centric workflows, ensuring that the ultimate review and decision-making remain in the hands of court personnel, thereby increasing overall efficiency within the judicial system. The user-friendly interface promotes better collaboration among court teams, leading to a more structured and organized method of managing cases. By enhancing communication and coordination among court staff, Learned Hand contributes to a more effective judicial process.

What is Intraspexion?

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.

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Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Not specified

Customer Service / Support

Web-Based Support

Training Options

Not specified

Training Options

Documentation Hub

Company Facts

Organization Name

Clio

Company Website

www.learned-hand.ai/

Company Facts

Organization Name

Intraspexion

Date Founded

2016

Company Location

United States

Company Website

intraspexion.com/mvp

Categories and Features

AI Legal

Not specified

Court Management

Not specified

Legal Case Management

Not specified

Categories and Features

AI Legal

Not specified

Legal

Not specified

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