ARTIFICIAL INTELLIGENCE
FOR USER-CENTRIC BUSINNESS

INSIGHTS DEVELOP SDK

Insights DEVELOP allows you to integrate SDK in your application. Gain new insights into your users and add new features and capabilities.

AUTOMOTIVE

Insights DEVELOP allows developers to integrate our cloud-based API into their existing mobile and web applications.

HEALTHCARE

Insights DEVELOP empowers intelligent patient diagnostics to help medical professionals to improve patient outcomes while reducing cost of care.

DEEP LEARNING
VISUAL INTELLIGENCE

How does it work? NVISO’s visual intelligence technology uses computers to learn from examples opposed to being manually programmed. Using deep Convolutional Neural Networks (CNNs) and state-of-the-art machine learning to understand human behaviors depicted in images and videos, it can achieve accuracy levels that surpass human performance in many narrowly defined tasks.

Expanding potentials. NVISO uses large training datasets containing millions of samples with proprietary software to detect, analyze and interpret behaviors from different learning scenarios (emotions, face identification, semantics, etc.). By continuously improving our datasets, algorithm training, and benchmarking capabilities we constantly expand visual intelligence applications across industries.


BEST-IN-CLASS
VISUAL INTELLIGENCE TECHNOLOGY

Scalable

CNNs and modern machine learning scale to learn from billions of examples resulting in an extraordinary capacity to learn highly complex behaviors and thousands of categories. Thanks to high volumes of data and powerful computing resources, NVISO intelligence technology can train powerful and highly accurate models.

Fast

Our trained models store their knowledge in a single network, making them easy to deploy in any environment. There is no need to store any additional data when new data is analyzed. This means that the NVISO visual intelligence engine can run on inexpensive devices with no internet connectivity providing responses in milliseconds.

Adaptive

NVISO visual technology approach does not require human engineered ad-hoc algorithms to extract the discriminative features in an image to make accurate predictions. Our algorithms are able to learn how to extract these meaningful features from the input using only the provided training data. This makes them easy to adapt to problems in any domain and evolve to new capabilities.

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