real-time invalid traffic detection, cross-channel fraud pattern identification, automated fraud blocking and traffic filtering, ad platform integration and deployment, granular fraud reporting and analytics, roi measurement and fraud cost quantification, bot activity classification and analysis, invalid traffic source attribution, multi-channel campaign fraud protection, fraud trend monitoring and alerting

Cheq

ProductPaid

AI-driven platform optimizing digital ads by preventing...

Best for:Mid-to-large advertising teams and agencies managing substantial PPC budgets across multiple channels who need immediate protection against sophisticated click fraud and invalid traffic.

/ 100

10 capabilities

Capabilities10 decomposed

real-time invalid traffic detection

Medium confidence

Analyzes incoming ad traffic in real-time to identify and classify bot activity, non-human clicks, and invalid impressions across display, video, and mobile channels. Uses machine learning models to distinguish legitimate user interactions from fraudulent ones.

Solves for

I want to stop paying for clicks from bots and fake usersI need to identify which traffic sources are sending invalid clicksI want to see patterns in fraudulent activity targeting my ads

Best for

mid-to-large advertising teams

digital marketing agencies

enterprises with substantial ad spend

Requires

active ad campaigns running on supported platforms

integration with Google Ads, Facebook, or programmatic exchanges

sufficient ad spend volume to generate meaningful data

Limitations

only detects fraud, does not optimize campaign performance

requires integration with ad platforms to function

effectiveness depends on quality of ML training data

cross-channel fraud pattern identification

Medium confidence

Detects and maps fraud patterns across multiple advertising channels simultaneously, identifying coordinated fraud schemes, common fraud sources, and recurring attack vectors. Provides granular reporting on specific fraud types and their origins.

Solves for

I want to understand which fraud patterns are hitting my campaignsI need to identify if the same fraudsters are attacking multiple channelsI want to see the geographic or source-based patterns in my fraud

Best for

agencies managing multi-channel campaigns

enterprises with complex ad portfolios

advertisers running campaigns across Google, Facebook, and programmatic

Requires

campaigns active on at least 2+ supported ad platforms

sufficient historical traffic data

integration with multiple ad networks

Limitations

requires data from multiple channels to identify patterns effectively

pattern detection improves over time as more data accumulates

cannot prevent fraud on channels not integrated with Cheq

automated fraud blocking and traffic filtering

Medium confidence

Automatically blocks identified fraudulent traffic and invalid impressions before they are charged to the advertiser's account. Filters traffic at the source level without requiring pixel implementations or manual configuration.

Solves for

I want fraudulent clicks automatically blocked so I don't pay for themI need to prevent invalid traffic from reaching my landing pagesI want to reduce wasted ad spend without manual intervention

Best for

advertisers wanting passive fraud protection

teams without dedicated fraud monitoring resources

campaigns requiring immediate fraud mitigation

Requires

platform integration with ad networks

sufficient traffic volume for ML model accuracy

acceptance of automated decision-making

Limitations

blocking decisions are automated and may occasionally block legitimate traffic

effectiveness depends on fraud detection accuracy

cannot block fraud on non-integrated platforms

ad platform integration and deployment

Medium confidence

Seamlessly integrates with major advertising platforms including Google Ads, Facebook, and programmatic exchanges without requiring pixel implementations or complex technical setup. Enables immediate fraud protection across integrated channels.

Solves for

I want to protect my ads without complex technical implementationI need Cheq to work with my existing ad platforms immediatelyI don't want to install tracking pixels or modify my ad setup

Best for

non-technical marketing teams

agencies wanting quick deployment

advertisers using major ad platforms

Requires

active accounts on supported ad platforms

API access or platform permissions

account authentication

Limitations

only works with supported platforms

integration quality may vary by platform

some advanced features may require additional setup

granular fraud reporting and analytics

Medium confidence

Provides detailed reporting dashboards showing fraud metrics, invalid traffic sources, fraud types, and performance impact. Enables advertisers to understand exactly where fraud is occurring and its financial impact on their campaigns.

Solves for

I want to see exactly how much fraud is costing meI need detailed reports on fraud sources and types for stakeholdersI want to track fraud trends over time to measure improvement

Best for

marketing managers needing stakeholder reporting

agencies billing clients for fraud protection

enterprises requiring compliance documentation

Requires

sufficient traffic data accumulated

access to Cheq dashboard

integration with ad platforms

Limitations

reporting is only as good as detection accuracy

historical data availability depends on how long Cheq has been monitoring

custom reports may require additional configuration

roi measurement and fraud cost quantification

Medium confidence

Calculates and tracks the financial impact of fraud prevention, showing how much wasted spend is being recovered and the return on investment from using the platform. Demonstrates measurable value through cost savings.

Solves for

I want to prove the value of fraud prevention to my CFOI need to calculate how much money Cheq is saving usI want to compare fraud costs before and after implementation

Best for

finance-conscious advertisers

agencies justifying tool costs to clients

enterprises requiring ROI documentation

Requires

sufficient historical data

accurate cost tracking

campaign spend data

Limitations

ROI calculation depends on accurate fraud detection

baseline fraud rates must be established for comparison

savings are relative to actual fraud that would have occurred

bot activity classification and analysis

Medium confidence

Identifies and categorizes different types of bot activity including click fraud bots, impression fraud bots, and other automated invalid traffic. Provides detailed classification of bot behavior patterns and sophistication levels.

Solves for

I want to understand what types of bots are attacking my campaignsI need to know if I'm facing sophisticated or simple bot fraudI want to see how bot activity has evolved over time

Best for

security-conscious advertisers

agencies managing high-value campaigns

enterprises needing detailed threat intelligence

Requires

sufficient traffic volume

historical data for pattern learning

integration with ad platforms

Limitations

bot classification accuracy improves with more data

new bot types may not be immediately recognized

classification is probabilistic, not deterministic

invalid traffic source attribution

Medium confidence

Traces fraudulent traffic back to its source, identifying which publishers, networks, or traffic sources are sending invalid clicks and impressions. Provides attribution data to help advertisers understand fraud origin points.

Solves for

I want to know which publishers are sending me fraudulent trafficI need to identify bad traffic sources to exclude themI want to understand if fraud is coming from specific networks or regions

Best for

advertisers using programmatic buying

agencies managing multiple publishers

campaigns requiring source-level optimization

Requires

traffic data with source information

integration with ad networks

sufficient volume for statistical accuracy

Limitations

attribution accuracy depends on available data signals

some fraud sources may be intentionally obfuscated

attribution is probabilistic

multi-channel campaign fraud protection

Medium confidence

Provides unified fraud protection across display, video, and mobile advertising channels simultaneously. Monitors and protects campaigns running on different channel types with consistent fraud detection standards.

Solves for

I want one solution protecting all my ad channelsI need consistent fraud detection across display, video, and mobileI want to manage fraud protection centrally instead of per-channel

Best for

multi-channel advertisers

agencies running diverse campaign types

enterprises with complex media mixes

Requires

active campaigns on multiple channels

integration with each channel's ad network

sufficient spend across channels

Limitations

protection quality may vary by channel

some channels may have different fraud patterns

requires integration with each channel's ad platform

fraud trend monitoring and alerting

Medium confidence

Continuously monitors fraud patterns and trends, alerting advertisers to sudden increases in fraud activity, new fraud types, or emerging threats. Enables proactive response to changing fraud landscapes.

Solves for

I want to be alerted when fraud suddenly increasesI need to know about new fraud patterns targeting my campaignsI want to stay ahead of emerging fraud threats

Best for

security-conscious advertisers

agencies managing high-value accounts

enterprises requiring proactive threat management

Requires

baseline fraud data established

alert configuration

integration with ad platforms

Limitations

alerts depend on detection accuracy

false positives may occur

alert sensitivity must be calibrated per account

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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Best For

✓mid-to-large advertising teams
✓digital marketing agencies
✓enterprises with substantial ad spend
✓agencies managing multi-channel campaigns
✓enterprises with complex ad portfolios
✓advertisers running campaigns across Google, Facebook, and programmatic
✓advertisers wanting passive fraud protection
✓teams without dedicated fraud monitoring resources

Known Limitations

⚠only detects fraud, does not optimize campaign performance
⚠requires integration with ad platforms to function
⚠effectiveness depends on quality of ML training data
⚠requires data from multiple channels to identify patterns effectively
⚠pattern detection improves over time as more data accumulates
⚠cannot prevent fraud on channels not integrated with Cheq

Requirements

active ad campaigns running on supported platformsintegration with Google Ads, Facebook, or programmatic exchangessufficient ad spend volume to generate meaningful datacampaigns active on at least 2+ supported ad platformssufficient historical traffic dataintegration with multiple ad networksplatform integration with ad networkssufficient traffic volume for ML model accuracy

Input / Output

Accepts: ad traffic data, click streams, impression logs, multi-channel traffic data, click metadata, impression data from different sources, real-time traffic streams, click data, platform credentials, account configuration, fraud detection data, traffic logs, campaign metadata, ad spend figures, campaign performance metrics, traffic behavior data, click patterns, impression metadata, traffic source data, display traffic data, video traffic data, mobile traffic data, real-time fraud detection data, historical fraud patterns

Produces: fraud classification labels, traffic quality scores, invalid traffic flags, fraud pattern reports, source attribution data, fraud type classifications, blocked traffic logs, filtered impression data, integrated fraud detection service, platform-native reporting, fraud reports, dashboard visualizations, metric summaries, export files, ROI calculations, cost savings reports, financial impact summaries, bot classifications, behavior analysis reports, threat intelligence summaries, source attribution reports, publisher fraud scores, network-level analysis, unified fraud reports, cross-channel metrics, consolidated dashboards, alert notifications, trend reports, anomaly flags

UnfragileRank

Adoption15%(30% weight)

Quality48%(25% weight)

Ecosystem15%(15% weight)

Match Graph10%(25% weight)

Freshness100%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Product

10 capabilities

Visit Cheq→

About

AI-driven platform optimizing digital ads by preventing fraud

Unfragile Review

Cheq leverages machine learning to detect and block invalid traffic, ad fraud, and bot activity in real-time, protecting advertising budgets from wasted spend. It's a specialized solution that directly addresses one of digital marketing's most persistent problems—fraud typically costs advertisers 15-20% of their media budgets. However, its narrow focus means it functions best as an add-on layer rather than a standalone platform.

Pros

+Real-time fraud detection across display, video, and mobile channels with granular reporting that identifies specific fraud patterns and sources
+Integrates with major ad platforms (Google Ads, Facebook, programmatic exchanges) without requiring pixel implementations, making deployment straightforward
+Demonstrates measurable ROI through fraud prevention, with many customers reporting 5-8x return on platform costs within 6-12 months

Cons

-Limited to fraud prevention only—doesn't optimize campaign performance, improve targeting, or increase conversion rates beyond preventing waste
-Pricing scales with ad spend and can become expensive for enterprises running massive campaigns; smaller advertisers may find per-campaign costs prohibitive

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Data Sources

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Capabilities10 decomposed

real-time invalid traffic detection

Medium confidence

Solves for

I want to stop paying for clicks from bots and fake usersI need to identify which traffic sources are sending invalid clicksI want to see patterns in fraudulent activity targeting my ads

Best for

mid-to-large advertising teams

digital marketing agencies

enterprises with substantial ad spend

Requires

active ad campaigns running on supported platforms

integration with Google Ads, Facebook, or programmatic exchanges

sufficient ad spend volume to generate meaningful data

Limitations

only detects fraud, does not optimize campaign performance

requires integration with ad platforms to function

effectiveness depends on quality of ML training data

cross-channel fraud pattern identification

Medium confidence

Solves for

Best for

agencies managing multi-channel campaigns

enterprises with complex ad portfolios

advertisers running campaigns across Google, Facebook, and programmatic

Requires

campaigns active on at least 2+ supported ad platforms

sufficient historical traffic data

integration with multiple ad networks

Limitations

requires data from multiple channels to identify patterns effectively

pattern detection improves over time as more data accumulates

cannot prevent fraud on channels not integrated with Cheq

automated fraud blocking and traffic filtering

Medium confidence

Solves for

I want fraudulent clicks automatically blocked so I don't pay for themI need to prevent invalid traffic from reaching my landing pagesI want to reduce wasted ad spend without manual intervention

Best for

advertisers wanting passive fraud protection

teams without dedicated fraud monitoring resources

campaigns requiring immediate fraud mitigation

Requires

platform integration with ad networks

sufficient traffic volume for ML model accuracy

acceptance of automated decision-making

Limitations

blocking decisions are automated and may occasionally block legitimate traffic

effectiveness depends on fraud detection accuracy

cannot block fraud on non-integrated platforms

ad platform integration and deployment

Medium confidence

Solves for

I want to protect my ads without complex technical implementationI need Cheq to work with my existing ad platforms immediatelyI don't want to install tracking pixels or modify my ad setup

Best for

non-technical marketing teams

agencies wanting quick deployment

advertisers using major ad platforms

Requires

active accounts on supported ad platforms

API access or platform permissions

account authentication

Limitations

only works with supported platforms

integration quality may vary by platform

some advanced features may require additional setup

granular fraud reporting and analytics

Medium confidence

Solves for

I want to see exactly how much fraud is costing meI need detailed reports on fraud sources and types for stakeholdersI want to track fraud trends over time to measure improvement

Best for

marketing managers needing stakeholder reporting

agencies billing clients for fraud protection

enterprises requiring compliance documentation

Requires

sufficient traffic data accumulated

access to Cheq dashboard

integration with ad platforms

Limitations

reporting is only as good as detection accuracy

historical data availability depends on how long Cheq has been monitoring

custom reports may require additional configuration

roi measurement and fraud cost quantification

Medium confidence

Solves for

I want to prove the value of fraud prevention to my CFOI need to calculate how much money Cheq is saving usI want to compare fraud costs before and after implementation

Best for

finance-conscious advertisers

agencies justifying tool costs to clients

enterprises requiring ROI documentation

Requires

sufficient historical data

accurate cost tracking

campaign spend data

Limitations

ROI calculation depends on accurate fraud detection

baseline fraud rates must be established for comparison

savings are relative to actual fraud that would have occurred

bot activity classification and analysis

Medium confidence

Solves for

I want to understand what types of bots are attacking my campaignsI need to know if I'm facing sophisticated or simple bot fraudI want to see how bot activity has evolved over time

Best for

security-conscious advertisers

agencies managing high-value campaigns

enterprises needing detailed threat intelligence

Requires

sufficient traffic volume

historical data for pattern learning

integration with ad platforms

Limitations

bot classification accuracy improves with more data

new bot types may not be immediately recognized

classification is probabilistic, not deterministic

invalid traffic source attribution

Medium confidence

Solves for

I want to know which publishers are sending me fraudulent trafficI need to identify bad traffic sources to exclude themI want to understand if fraud is coming from specific networks or regions

Best for

advertisers using programmatic buying

agencies managing multiple publishers

campaigns requiring source-level optimization

Requires

traffic data with source information

integration with ad networks

sufficient volume for statistical accuracy

Limitations

attribution accuracy depends on available data signals

some fraud sources may be intentionally obfuscated

attribution is probabilistic

multi-channel campaign fraud protection

Medium confidence

Solves for

I want one solution protecting all my ad channelsI need consistent fraud detection across display, video, and mobileI want to manage fraud protection centrally instead of per-channel

Best for

multi-channel advertisers

agencies running diverse campaign types

enterprises with complex media mixes

Requires

active campaigns on multiple channels

integration with each channel's ad network

sufficient spend across channels

Limitations

protection quality may vary by channel

some channels may have different fraud patterns

requires integration with each channel's ad platform

fraud trend monitoring and alerting

Medium confidence

Solves for

I want to be alerted when fraud suddenly increasesI need to know about new fraud patterns targeting my campaignsI want to stay ahead of emerging fraud threats

Best for

security-conscious advertisers

agencies managing high-value accounts

enterprises requiring proactive threat management

Requires

baseline fraud data established

alert configuration

integration with ad platforms

Limitations

alerts depend on detection accuracy

false positives may occur

alert sensitivity must be calibrated per account

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Unfragile Review

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Cheq

Capabilities10 decomposed

real-time invalid traffic detection

cross-channel fraud pattern identification

automated fraud blocking and traffic filtering

ad platform integration and deployment

granular fraud reporting and analytics

roi measurement and fraud cost quantification

bot activity classification and analysis

invalid traffic source attribution

multi-channel campaign fraud protection

fraud trend monitoring and alerting

Related Artifactssharing capabilities

DataVisor

Riskified

Transparently.AI

BforeAI

Arya.ai

Baselayer

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Unfragile Review

Pros

Cons

Categories

Alternatives to Cheq

Are you the builder of Cheq?

Get the weekly brief

Data Sources

Cheq

Capabilities10 decomposed

real-time invalid traffic detection

cross-channel fraud pattern identification

automated fraud blocking and traffic filtering

ad platform integration and deployment

granular fraud reporting and analytics

roi measurement and fraud cost quantification

bot activity classification and analysis

invalid traffic source attribution

multi-channel campaign fraud protection

fraud trend monitoring and alerting

Related Artifactssharing capabilities

DataVisor

Riskified

Transparently.AI

BforeAI

Arya.ai

Baselayer

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Unfragile Review

Pros

Cons

Categories

Alternatives to Cheq

Are you the builder of Cheq?

Get the weekly brief

Data Sources