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Description

The annual expenses associated with commercial tort litigation targeting businesses, which encompass benefits paid, losses, legal fees, and administrative expenditures such as document collection and attorney meetings, were estimated at around $160 billion, culminating in nearly $1.6 trillion over the course of a decade. To create a deep learning model focused on the federal court’s civil rights-employment category, specifically "employment discrimination," we gathered factual allegations from 400 federal court complaints—excluding any email data. This model was deployed on GPU instances within Microsoft Azure and AWS, where it was evaluated using 20,401 emails from the Enron dataset, marking the first instance the model encountered email data. Each identified true positive email can be exported to a platform for internal investigations or case management purposes, enhancing the model’s utility. Furthermore, with an integrated database connected to the user interface, users have the capability to save these true positives, incorporate them into the initial training set, and subsequently re-train the model for improved accuracy and performance. As a result, this continual learning process ensures that the model evolves and adapts over time to better identify relevant cases.

Description

LegalMation offers a range of innovative generative AI tools designed to empower litigation attorneys and other legal professionals to maximize their effectiveness. Among these tools is our groundbreaking Discovery Response Creator, which revolutionizes a traditionally labor-intensive aspect of legal work: crafting responses to written discovery requests. In the absence of our solution, legal professionals often face the cumbersome process of extracting discovery requests from the original PDF provided by opposing counsel, which involves tedious tasks such as correcting formatting errors or even retyping the entire text to create a response template. Following this, attorneys or paralegals must carefully review each request, identify applicable objections, and manually input the appropriate responses. Given the volume of discovery requests, this process can consume anywhere from one to three hours for each set of requests, not accounting for the additional time needed to formulate substantive replies to those inquiries. Consequently, our tool significantly enhances efficiency and accuracy in the discovery process, allowing legal teams to focus on more critical aspects of their cases.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

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Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Intraspexion

Founded

2016

Country

United States

Website

intraspexion.com/mvp

Vendor Details

Company Name

LegalMation

Website

www.legalmation.com

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