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Description

Astra for Law serves as an innovative AI platform for legal firms and technology providers, enabling the development of specialized products and workflows that leverage their legal expertise. It integrates the capabilities of GPT-6 Astra with specific settings, tools, and contextual elements designed specifically for the legal profession, including a comprehensive legal search index along with guidance for legal analysis and writing tasks. This platform is capable of searching through U.S. case law, statutes, regulations, court rules, and administrative decisions, drawing from an extensive database of over 230 million URLs that are continuously updated. Its primary function is to identify pertinent authorities and passages, evaluate their relevance and binding nature, and apply this research effectively to various client-related facts, arguments, deal terms, and strategic legal considerations. Additionally, it offers custom instructions that help differentiate between court holdings and ancillary observations, tackle authorities that may undermine an argument, clarify contractual risks, and pinpoint areas of uncertainty. Legal firms can tailor their workflows to align with their own precedents, methodologies, proprietary information, approved sources, and review protocols, ensuring a personalized approach to legal work that enhances efficiency and effectiveness. This level of customization empowers firms to maintain their unique practices while benefiting from advanced AI capabilities.

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.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

GPT-6 Astra Yes 
GPT-6 Luna Yes 
GPT-6 Sol Yes 
GPT-6.1 Sol Yes 
Harvey AI Yes 
Holo4 Yes 
Legora Yes 
OpenAI Yes 

Integrations

GPT-6 Astra No 
GPT-6 Luna No 
GPT-6 Sol No 
GPT-6.1 Sol No 
Harvey AI No 
Holo4 No 
Legora No 
OpenAI No 

Pricing Details

No price information available.
Free Trial No 
Free Version Yes 

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

OpenAI

Founded

2015

Country

United States

Website

openai.com/index/astra-for-law/

Vendor Details

Company Name

Intraspexion

Founded

2016

Country

United States

Website

intraspexion.com/mvp

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