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What is Omnisient?

We enable organizations to leverage the capabilities of first-party data collaboration while effectively reducing any potential risks involved. By transforming consumer data from a possible liability into a lucrative asset, companies can thrive in an evolving landscape that has surpassed traditional cookie usage. Our method promotes stronger partnerships with additional collaborators, which in turn amplifies value for clients. Additionally, we promote financial inclusion and enhance revenue through pioneering alliances that tap into alternative data resources. Our solution improves the precision of underwriting and increases profitability by integrating these varied data streams. Each participant utilizes our secure desktop application to anonymize, tokenize, and protect all personally identifiable information within their consumer data, ensuring it stays secure in their local setting. This process generates US-patented crypto-IDs for every anonymized consumer profile, facilitating the secure linking of shared consumers across multiple datasets within our neutral Cloud infrastructure. By employing our state-of-the-art technology, we are leading the charge in the next phase of consumer data management and collaboration. This guarantees that businesses can prosper in a data-centric future while adhering to the utmost standards of privacy and security, ultimately fostering greater trust and transparency in the marketplace.

What is Apheris?

Apheris serves as a platform that enables organizations to securely and privately collaborate on distributed datasets while remaining compliant with regulatory frameworks. By utilizing the Apheris Compute Gateway, users can conduct machine learning and analytical tasks directly where the data resides, ensuring that it is never exposed or relocated, which helps maintain its intrinsic value. This innovative approach addresses common issues associated with data being confined due to geographical limitations, regulatory constraints, or internal organizational policies, as well as the difficulties that arise from transferring sensitive or costly data. Unlike alternative methods such as synthetic data generation, encryption, or data clean rooms—which can compromise result accuracy, introduce security risks, or lack the capacity to scale effectively—Apheris adopts a federated model. This enables the creation of analytical models from extensive datasets without necessitating data movement, backed by a comprehensive framework of governance, security, and privacy that guarantees adherence to applicable regulations from the very beginning. As a result, Apheris not only fosters enhanced collaboration but also plays a crucial role in protecting essential data assets, making it an invaluable resource for organizations navigating the complexities of data utilization. Furthermore, this capability positions Apheris as a forward-thinking solution in an increasingly data-driven world.

Media

Media

Integrations Supported

Azure Machine Learning
Jupyter Notebook
Microsoft Power BI
Python

Integrations Supported

Azure Machine Learning
Jupyter Notebook
Microsoft Power BI
Python

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Omnisient

Date Founded

2019

Company Location

South Africa

Company Website

omnisient.com

Company Facts

Organization Name

Apheris

Company Location

Germany

Company Website

www.apheris.com

Categories and Features

Categories and Features

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