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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.

What is EquityPath?

Examine, evaluate, and address potential wage disparities linked to race and gender among employees in similar positions by reviewing their salary histories. It is crucial to swiftly implement adjustments to rectify any identified discrimination and to create a fair Reduction-In-Force (RIF) strategy that minimizes the risk of legal repercussions during layoffs and furloughs. EquityPath RIF serves as a specialized online tool designed to tackle equity concerns related to RIF scenarios, such as layoffs and furloughs. The platform boasts an intuitive user interface, sophisticated econometric models, and an easy-to-navigate dashboard that helps identify, measure, and correct potential discriminatory practices affecting diverse races and genders among employees in comparable roles. By utilizing EquityPath RIF, organizations can efficiently address emerging issues of discrimination and develop a RIF strategy that is not only equitable but also lowers the chance of litigation, thereby promoting a more just workplace culture. This software equips employers with the necessary tools to tackle intricate equity dilemmas with both assurance and transparency, ultimately contributing to a more inclusive environment where all employees feel valued. In doing so, it empowers organizations to foster lasting change and support equal opportunities for all staff members.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$21 per month
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Intraspexion

Date Founded

2016

Company Location

United States

Company Website

intraspexion.com/mvp

Company Facts

Organization Name

EquityPath

Company Website

www.equitypath.com

Categories and Features

Categories and Features

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