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Description
CaseEnsemble stands as the flagship product of Xerdict, serving as an exclusive web-based collaboration platform for law firms focused on managing legal matters. This sophisticated legal extranet enables teams from various organizations and remote locations to access a unified, secure online space for tracking case information, coordinating a shared calendar, collaborating on documents, and sharing completed work products. With its user-friendly and readily customizable features, CaseEnsemble can effectively tackle even the most complicated litigation challenges in a matter of hours. The platform encompasses a range of legal case management and practice management tools, equipped with software modules suitable for any litigation practice type. Additionally, CaseEnsemble presents several tailored configurations for case management that allow users to organize issue-specific and practice-specific data fields. Furthermore, Xerdict’s collaboration tools empower clients to personalize their solutions to better meet their unique needs. This flexibility enhances the overall effectiveness and adaptability of legal practices utilizing CaseEnsemble.
Description
KServe is a robust model inference platform on Kubernetes that emphasizes high scalability and adherence to standards, making it ideal for trusted AI applications. This platform is tailored for scenarios requiring significant scalability and delivers a consistent and efficient inference protocol compatible with various machine learning frameworks. It supports contemporary serverless inference workloads, equipped with autoscaling features that can even scale to zero when utilizing GPU resources. Through the innovative ModelMesh architecture, KServe ensures exceptional scalability, optimized density packing, and smart routing capabilities. Moreover, it offers straightforward and modular deployment options for machine learning in production, encompassing prediction, pre/post-processing, monitoring, and explainability. Advanced deployment strategies, including canary rollouts, experimentation, ensembles, and transformers, can also be implemented. ModelMesh plays a crucial role by dynamically managing the loading and unloading of AI models in memory, achieving a balance between user responsiveness and the computational demands placed on resources. This flexibility allows organizations to adapt their ML serving strategies to meet changing needs efficiently.
API Access
Has API
API Access
Has API
Integrations
Bloomberg
Docker
Gojek
IBM Cloud
Kubeflow
Kubernetes
NAVER
NVIDIA DRIVE
ZenML
Zillow
Integrations
Bloomberg
Docker
Gojek
IBM Cloud
Kubeflow
Kubernetes
NAVER
NVIDIA DRIVE
ZenML
Zillow
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
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
Xerdict
Website
www.xerdict.com/products.php
Vendor Details
Company Name
KServe
Website
kserve.github.io/website/latest/
Product Features
Legal Case Management
Billing Management
Calendar Management
Case Notes
Client Management
Communication Tracking
Conflict Management
Corporations
Court Management
Discovery Management
Docket Management
Document Management
Expense Tracking
Government
Law Firms
Records Management
Task Management
Time Tracking
Trust Accounting
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
