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features
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support

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Description

Apheris serves as a collaborative platform that allows organizations to work together on distributed data in a manner that is secure, private, and adheres to regulatory standards. By utilizing the Apheris Compute Gateway in conjunction with your data, machine learning and analytics processes occur directly at the data source, preventing any movement or direct accessibility of the data, thereby preserving its inherent value. This innovative methodology resolves common issues associated with data silos that arise from geographical, regulatory, or organizational constraints, as well as situations where data is too sensitive or expensive to transport. Unlike other methods such as synthetic data generation, encryption, or data clean rooms—which may compromise the validity of results, introduce risks of data breaches, or lack scalability—Apheris employs a federated approach to develop models across entire data cohorts without transferring any actual data. With a foundation built on governance, security, and privacy, Apheris guarantees compliance with regulations from the outset, enabling organizations to leverage their data assets more effectively. Ultimately, this unique strategy not only enhances data usability but also instills confidence among stakeholders regarding data protection and regulatory adherence.

Description

We assist developers in addressing critical global challenges by maximizing the potential of sensitive data while minimizing associated risks. This motivation drives us to create privacy-focused tools for machine learning and analytics tailored for the evolving landscape of distributed data. Various forms of data are continuously produced and kept in cloud environments, on-site locations, and increasingly at the network's edge. The financial burden of de-identifying, transferring, centrally storing, and managing vast amounts of data can often be overwhelming. Regulations such as HIPAA, GDPR, PIPEDA, and CCPA impose restrictions on the ways in which data can be aggregated, particularly across different regions. By utilizing federated learning and analytics, we ensure that only model parameters are transmitted from each private server, allowing data custodians to maintain complete control over their information. By leveraging this innovative approach, businesses can enhance their offerings to existing clients through the development of new features that tap into the shared insights derived from customer data. This way, organizations can not only comply with regulations but also drive growth in a secure and efficient manner.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial No 
Free Version No 

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 Yes 
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) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Apheris

Country

Germany

Website

www.apheris.com

Vendor Details

Company Name

integrate.ai

Founded

2017

Country

Canada

Website

www.integrate.ai/

Product Features

Artificial Intelligence

Chatbot No 
For Healthcare No 
For Sales Yes 
For eCommerce Yes 
Image Recognition No 
Machine Learning No 
Multi-Language No 
Natural Language Processing No 
Predictive Analytics Yes 
Process/Workflow Automation No 
Rules-Based Automation No 
Virtual Personal Assistant (VPA) No 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Alternatives

Alternatives

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