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The True Test Of Intelligence:
Using AI To Test AI

Businesses need to trust that the core AI algorithms and logic produce the expected outcomes.

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Goodbye hype. AI is real.

Businesses are increasingly adopting AI applications like Microsoft Copilot and Salesforce Einstein to revolutionize their operational frameworks, creating a more efficient, innovative, and customer-focused environment. But what happens when AI isn’t working as expected? How do you ensure your AI isn’t hallucinating?

Article: Testing of AI Applications with AI-augmented Tools

Generative AI risks

Trust and reliability issues

Trust and reliability issues

Errors erode users' trust in the AI, degrading customer experience and business performance.

Data exposure

Data exposure

Malfunctions may lead to inadvertent exposure or mishandling of sensitive data.

Compromised quality

Compromised quality

Incorrect suggestions or completions can result in errors impacting business operations.

Compliance risks

Compliance risks

Errors in AI processing could lead to non-compliance with data privacy regulations, posing legal risks.

Complex testing made simple

You want your teams to deliver higher-quality software faster and more effectively. To meet this challenge, you may be using or considering AI-augmented testing tools. Let us show you how you drive continuous quality across your business – including your AI apps.

 
AI Validate

AI Validate

Compares AI-generated responses with expected outcomes to ensure accuracy.

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AI Vision Validate

AI Vision Validate

Enhance UI validation workflows with greater precision with AI-powered text and data validation.

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AI Generate

AI Generate

Creates realistic and varied datasets for more comprehensive testing, simulating real-world scenarios.

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AI Transform

AI Transform

Allows for complex data manipulations tailored to specific needs, enhancing data utility within tests.

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AI Extract

AI Extract

Simplifies the extraction of data from various inputs, making your data handling more streamlined.

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AI Vision Extract

AI Vision Extract

Extract key data from visual elements using an image or screenshot for enhanced accuracy.

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Abhay Parasnis

"The world of enterprise software is going to get completely rewired. Companies with untrustworthy AI will not do well in the market."

Source: Salesforce + Series – Ask more of AI

AI and Software Quality: Trends and Executive Insights

This report provides decision-makers with a comprehensive overview of AI adoption, highlighting the critical need for testing AI applications and the growing role of AI-augmented testing tools.

It offers essential insights and solutions for businesses to adapt and consistently deliver exceptional use and customer experiences at scale.

Download the report
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AI application use case examples

Customer Service

Customer service

Natural language processing (NLP) enable chat-bots to understand and respond to customer queries effectively.

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Supply Chain Optimization

Supply chain optimization

Machine learning models analyze historical sales data, market trends, and other factors to forecast demand and optimize stock levels, reducing overstock and stockouts.

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Supply Chain Optimization

Process automation

Robotic Process Automation (RPA) combined with AI automates routine and repetitive tasks such as data entry, invoice processing, and employee onboarding.

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Supply Chain Optimization

Data analysis and insights

AI analyzes large datasets to uncover patterns and insights that inform decision-making.

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Customer Service

Fraud detection and cybersecurity

AI algorithms detect fraudulent activities by analyzing transaction patterns and identifying anomalies and identifying potential threats in real-time. 

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Supply Chain Optimization

Energy management

AI optimizes energy consumption by analyzing usage patterns and adjusting systems for efficient energy use. 

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Supply Chain Optimization

Technology and consumer goods

AI accelerates product development by analyzing customer feedback and market trends to suggest new features or product ideas.

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Supply Chain Optimization

Predictive maintenance

Sensors collect data on machinery, and AI algorithms analyze this data to forecast maintenance needs, reducing downtime and repair costs.

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