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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

Unmatched scope – Access detailed results at both the county and global levels with ease. Premonition’s reach surpasses that of LexisNexis, Thomson Reuters, and Bloomberg combined. Limitless detail – Filter by venue, case type, judge, attorney, specific docket entries (like trial set dates), or keywords within documents. Data can be imported in API or CSV format directly into Salesforce or your CRM, eliminating the tedious cut-and-paste tasks required by competitors. Enhanced filtering – There’s no need to employ people to sift through court documents; our system can process 50,000 pages in less than a second. Acquire precisely the information you require and only incur costs for what you actually use. Discover the attorneys and legal cases that align best with your investment criteria weeks ahead of traditional lead services. With automated court monitoring for individuals or entities of interest, the efficiency of thousands of agents is replicated through automation, streamlining the process significantly. This allows for a more proactive approach in identifying valuable legal opportunities.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Intraspexion

Founded

2016

Country

United States

Website

intraspexion.com/mvp

Vendor Details

Company Name

Premonition

Founded

2014

Country

United States

Website

premonition.ai/

Product Features

Product Features

Legal Research

AI / Machine Learning No 
Annotations No 
Brief Analytics No 
Case Alerts No 
Case Law Research No 
Change Tracking No 
Data Visualization No 
Filtering No 
Query Suggestions No 
Search History No 
Self-Service Search No 
Statutes Research No 
Tax Law Research No 
Third-Party Analysis Integration No 

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Alternatives

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