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Description
Achieving commercial value in just weeks, ElectrifAi effectively addresses high-value use cases across various industries. With the most extensive collection of pre-built machine learning models available, our solutions integrate effortlessly into your current workflows, yielding swift and dependable outcomes. You can benefit from our specialized knowledge through pre-trained, pre-structured, or entirely new models tailored to your needs. Developing machine learning systems can be fraught with challenges and take considerable time, but ElectrifAi offers a superior approach by delivering over 1,000 ready-to-deploy models that integrate smoothly into existing processes. Our capabilities extend to deploying proven machine learning models quickly, ensuring that you receive solutions without delay. We handle the creation of machine learning models, the data ingestion process, and the necessary data cleansing. Our team of domain experts collaborates with your existing data to train the most suitable model for your specific use case, ensuring optimal performance and efficiency. By leveraging our expertise, you can unlock the full potential of your data and turn insights into actionable strategies.
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
API Access
Has API
Integrations
AWS Marketplace
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
ElectrifAi
Founded
2004
Country
United States
Website
www.electrifai.net
Vendor Details
Company Name
Intraspexion
Founded
2016
Country
United States
Website
intraspexion.com/mvp
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization