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What is Mage?

Mage is an innovative platform that enables the effortless transformation of your data into actionable predictions. In only a few minutes, you can build, train, and deploy predictive models without any need for previous AI knowledge. By effectively ranking content on user home feeds, you can significantly enhance user engagement. Additionally, displaying the most relevant products tailored to individual users can lead to improved conversion rates. Mage also helps in predicting which users might stop using your application, thereby aiding in user retention. Furthermore, it facilitates better conversions by accurately matching users within a marketplace. The success of AI is rooted in data quality, and Mage is designed to guide you through this process, offering valuable tips to refine your data and boost your AI skills. While understanding AI and its predictions can be daunting, Mage simplifies the experience by providing clear explanations of each metric, enabling you to comprehend how your AI model functions. With just a few lines of code, you can obtain real-time predictions and smoothly integrate your AI model into any application, making the process not only efficient but also user-friendly for everyone. This all-encompassing approach guarantees that you are utilizing AI to its fullest potential while also acquiring insights that can propel your business forward, ultimately making data-driven decision-making a reality for all users.

What is Infer?

Infer is a software-as-a-service platform that harnesses artificial intelligence, seamlessly connecting with a range of MarTech solutions to provide sophisticated predictive lead and account scoring, along with the management of highly detailed profiles. By accurately identifying the most promising leads, businesses can develop highly targeted segments and personalize their outreach efforts on a grand scale, resulting in a notable increase in conversion rates. The platform features advanced predictive lead scoring and profile management tools tailored to enhance and optimize sales and marketing operations. It enables the real-time assessment of large quantities of incoming leads, contacts, and accounts, which ultimately boosts return on investment by refining marketing qualified leads and steering sales focus toward those with the greatest revenue potential. Additionally, Infer supports fair and effective lead distribution among sales teams and assesses campaign performance by market segment, fostering ongoing enhancements in targeting and segmentation tactics. As organizations leverage these robust capabilities, they can ensure not only the identification of high-value prospects but also the efficient maximization of their outreach strategies, which is critical in today’s competitive landscape. This comprehensive approach empowers businesses to stay ahead of the curve in attracting and retaining customers.

Media

Media

Integrations Supported

CodeSign Secure
Python
R
SQL
Terraform

Integrations Supported

Adobe Marketo Engage
HubSpot CRM
HubSpot Customer Platform
HubSpot Marketing Hub
Synoptos
WordPress

API Availability

API Availability

Pricing Information

Free
Open source
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

Mage

Date Founded

2020

Company Location

United States

Company Website

www.mage.ai/

Company Facts

Organization Name

IgniteTech

Company Location

United States

Company Website

ignitetech.com/softwarelibrary/infer

Categories and Features

Data Pipeline

Not specified

Predictive Analytics

Not specified

Categories and Features

Demand Generation

Not specified

Lead Generation

Lead Scoring

Lead Management

Lead Scoring

Lead Scoring

Not specified

Predictive Analytics

AI / Machine Learning
Demand Forecasting
Sentiment Analysis

Sales Tracking

Not specified

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