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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
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