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What is Scalarr?

Scalarr presents an innovative solution for detecting mobile ad fraud by leveraging state-of-the-art Machine Learning technology. To address the most pressing issues in mobile advertising, Scalarr implements a dual-layered strategy featuring next-generation algorithms that boast an outstanding accuracy rate of up to 97% in recognizing various types of in-app fraud. Users can take advantage of Scalarr’s offerings by delving into its capabilities to monitor, assess, and prevent mobile app install ad fraud through its unsupervised machine learning functionalities before any damage occurs. The platform effectively employs both unsupervised and semi-supervised machine learning methods to autonomously identify and comprehend fraud patterns within extensive datasets. By analyzing numerous clicks, installs, and post-install event variables, Scalarr greatly reduces the occurrence of both false positives and false negatives in its detection framework. With a sophisticated model designed to prioritize accuracy and thoroughness, Scalarr emerges as a powerful tool that delivers actionable insights at the individual conversion level, enabling advertisers to make well-informed decisions about their ad campaigns. This holistic methodology not only fortifies the integrity of mobile advertising efforts but also empowers marketers to optimize their strategies for better performance and outcomes. Ultimately, Scalarr’s commitment to innovation ensures a safer advertising environment for all stakeholders involved.

What is Hoopoz?

Hoopoz is a cutting-edge AI platform specifically engineered to monitor ad compliance, track affiliate advertisements, identify creators, and eliminate illegal competitor ads, particularly in regulated industries where non-compliance poses serious legal, financial, and performance risks. The platform continuously scans the marketplace to remove unauthorized advertisements while guaranteeing that compliant brands attain the visibility they deserve. When unethical competitors flood the market with misleading claims, lack of disclosures, harmful content, or deceptive advertising, the cost-per-click (CPC) often rises, resulting in compliant ads becoming less visible; Hoopoz automates the cleansing of such environments while maintaining user anonymity. This advanced system thoroughly evaluates every advertisement across a variety of key channels and platforms, including Meta, Google Ads, TikTok, YouTube, Instagram, X, Twitch, Facebook, Rumble, Kick, Snapchat, and LinkedIn. In addition, Hoopoz Defend meticulously scrutinizes all components, such as frames, audio tracks, captions, overlays, images, affiliate links, and creator posts, to pinpoint any violations of policies, missing FTC disclosures, unsafe health or financial claims, prohibited imagery, and brand mentions, thus fostering a safer advertising environment. By offering these extensive features, Hoopoz not only protects brands but also encourages a more transparent and accountable advertising ecosystem, ultimately benefiting both businesses and consumers alike.

Media

Media

Integrations Supported

Adjust
AppsFlyer
Branch
Kochava
Singular
Tenjin
mParticle

Integrations Supported

Facebook
Google Ads
Instagram
Kick
LinkedIn
Meta AI
Rumble
Snapchat
TikTok
Twitch
X (Twitter)
YouTube

API Availability

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

€1,000 per month

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

Scalarr

Date Founded

2016

Company Location

United States

Company Website

scalarr.io

Company Facts

Organization Name

Hoopoz

Date Founded

2024

Company Location

Lithuania

Company Website

www.hoopoz.com

Categories and Features

Ad Fraud Detection

Not specified

Click Fraud

Not specified

Categories and Features

Ad Fraud Detection

Not specified

Click Fraud

Not specified

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