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What is Litmas AI?

Litmas AI stands out as a sophisticated litigation platform tailored exclusively for legal experts, distinguishing itself from other AI tools that may compromise on accuracy by guaranteeing that its research, motion drafting, and case analysis yield precise and court-ready results, with no instances of hallucination in citations. Among its key offerings are the AI Associate, which delivers customized responses backed by verified citations for in-depth legal analysis and strategic development; the Motion Builder, capable of producing court-ready motions in mere minutes with a remarkable 90% success rate, also supported by validated citations; and the Litiverse Graph, which visually represents the relationships among parties, witnesses, claims, and pertinent facts. Additionally, the Evidence Mapper links factual elements to legal components, clarifying what is substantiated and identifying gaps, while the Pre-Suit Module simplifies the drafting of compliant complaints and demand letters. What truly differentiates Litmas AI is its design by trial attorneys specifically for the litigation process rather than relying on a general AI framework. Our rigorous multi-step verification approach significantly reduces inaccuracies, ensuring that legal professionals can trust the information they receive. With comprehensive training on verified U.S. case law, the platform streamlines the workflow from case intake through to motion preparation. Furthermore, the absence of invented citations enhances the reliability of our research, fostering a level of confidence that legal practitioners can depend on to improve their practice and deliver better results for their clients. As a result, Litmas AI not only optimizes legal workflows but also empowers attorneys to focus more on their clients and less on administrative tasks.

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.

Media

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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

Pricing not provided.
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

Litmas AI

Date Founded

2025

Company Location

United States

Company Website

litmas.ai/

Company Facts

Organization Name

Intraspexion

Date Founded

2016

Company Location

United States

Company Website

intraspexion.com/mvp

Categories and Features

Categories and Features

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