Get insights on any design with just one click.

EyeQuant is an A.I. that gives you instant, objective feedback on web & mobile designs. Run smarter A/B tests, validate UX ideas and present your design decisions with data. 

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Trusted by world-class UX and Conversion teams at:

Trusted By World-Class UX And Conversion Teams

The world's fastest AI-based design audit

Perception Map

Instantly see what your users will
see by analyzing attention.

EyeQuant predicts which parts of your design are most and least eye-catching, so you can make sure that your most important content is visible right away.

Example: See how Groupon increased conversions by 52%

Visual Clarity Map

Get instant design metrics on
visual clarity and excitingness.

EyeQuant rates how clear and exciting your designs are,
so you can benchmark and identify subpar designs.

Example: See how Epson increased conversions by over 20%

How does EyeQuant help me?

Find visual design flaws

Visual Flaws

Validate and improve designs pre-launch

Validate Designs

Justify decisions and win support for ideas

Decisions Ideas

Benchmark your designs, best-in-class and your competitors

Analyze and take a detailed look at best-in-class and competitors to learn from their designs

Learn more from your A/B tests

Inspect your winning (and losing) test designs to learn what works and not

Set “visual KPIs” to create clear goals for your design team

Agree on what should be prioritized in the design and use EyeQuant to make sure you’ll hit those goals

How does EyeQuant work?

Our scientific process

EyeQuant’s researchers and scientific advisors are pioneers in the field of computational neuroscience, and the team’s work in relation to visual processes and modeling has been published in numerous peer-reviewed academic journals. Our predictive design analysis is built through a rigorous process of data collection, statistical analysis and machine learning.

In-depth accuracy information

Scientific Advisory Board

We invented, developed and patented EyeQuant with brain researchers at the California Institute of Technology, the University of Southern California and the University of Osnabrück, Germany.

Christof Koch

Professor Christof Koch, PhD

Scientific Advisor

Peter König

Professor Dr. Peter König

Co-Founder and Scientific Director

Laurent Itti

Professor Laurent Itti, PhD

Scientific Advisor

90% accuracy

How we build EyeQuant

We Collect Data On How People See And Perceive Websites

1. We collect data on how people see and perceive websites

In order to build predictions about how users will react to a specific design, we first need to have a detailed understanding of how people actually see the web. To do this, we’ve conducted user studies and experiments with thousands of participants. We use eye-tracking equipment (in Osnabrück, Germany) to measure exactly how users look at websites, and we ask users to rate and compare website designs by asking questions like “which of these 2 designs feels cleaner and more organised?”

Statistical analysis and machine learning

2. Statistical analysis and machine learning

After gathering millions of data points in our research, we use statistical analysis to find correlations between specific design characteristics and user behaviours. For example, we know that (all else equal), colour contrast tends to catch users’ eyes, and we know that the number of edges in a design can help us predict whether or not a design is too cluttered. Once we have a list of design characteristics that affect behaviour, we use these as variables in predictive models. We use machine learning to determine the optimal weight and combinations of these design characteristics. By comparing predictions with real user data, we can evaluate the accuracy of our models.

The EyeQuant Web Service

3. The EyeQuant Web Service

Once we’ve got an accurate predictive model, we offer cloud-based access via the EyeQuant web app. When you upload a design, EyeQuant takes just seconds to return results. During that time, EyeQuant identifies important design characteristics, and assigns scores for around 50 different predictive variables to each pixel on the page to get a clear picture of how users will react to this specific design.