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What is Viven?

Viven creates bespoke "Digital Twins" for employees by developing customized language models that reflect their real work activities, including emails, meetings, documents, and chat exchanges, thus enabling these digital counterparts to replicate the individual's thought processes, writing nuances, and behavioral patterns. Acting as a constant companion, the twin retains crucial information, prepares users for future meetings, nudges teams when projects face delays, drafts follow-up communications, and allows colleagues to ask questions directly, thereby maintaining workflow efficiency even when the original employee is unavailable. The platform provides enterprise-level deployment options, supporting SaaS, private VPC, or on-premises configurations, all featuring detailed role-based access controls, extensive audit trails, and strong data governance measures. Furthermore, Viven integrates effortlessly with a variety of tools such as Gmail, Slack, Microsoft Teams, Outlook, Google Drive, OneDrive, Jira, Salesforce, among others, which equips the twin with a comprehensive understanding of the user's work context. This integration significantly boosts productivity by enabling the twin to operate seamlessly across multiple applications, ensuring that the employee's influence remains present even during their absence. Additionally, the approach not only streamlines communication but also fosters a collaborative environment, enhancing team dynamics and project outcomes.

What is Unlearn?

Advancing artificial intelligence to eliminate the necessity for trial and error in healthcare, our digital twins enable rapid and reliable clinical trials. We prioritize research in fields such as neuroscience, immunology, and metabolic disorders, among others. TwinRCTs accelerate full enrollment by requiring fewer participants to achieve the same statistical power as traditional trial methods. This innovative strategy drastically cuts down on the time needed for late-stage study enrollment. Furthermore, TwinRCTs improve the detection of treatment effects in early-stage research by increasing statistical power without needing more participants. They allow researchers to make well-informed decisions based on initial findings and assist in attracting additional participants to clinical trials. By utilizing smaller control groups, TwinRCTs also enhance the likelihood of participants receiving the experimental treatment. Our dedication to ensuring that clinical trials with digital twins achieve regulatory approval remains steadfast. Unlearn is leading the charge in revolutionizing the healthcare industry through the pioneering use of artificial intelligence, crafting and applying cutting-edge generative models that utilize extensive datasets gathered from prior patient studies. This shift in methodology not only optimizes research efficiency but also significantly improves the effectiveness of clinical trials. The future of medicine is being reshaped as we continue to refine these innovative approaches.

Media

Media

Integrations Supported

Box
Cisco Webex
Confluence
GitHub
Gmail
Google Calendar
Google Drive
Google Meet
Jira
Microsoft OneDrive
Microsoft Outlook
Microsoft SharePoint
Microsoft Teams
Salesforce
Slack
Zoom

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

Viven

Date Founded

2025

Company Location

United States

Company Website

viven.ai/

Company Facts

Organization Name

Unlearn

Company Location

United States

Company Website

www.unlearn.ai/

Categories and Features

Agentic AI

Not specified

AI Agents

Not specified

AI Productivity

Not specified

AI Tools

Not specified

Digital Twin

Not specified

Categories and Features

Digital Twin

Not specified

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