SparkBeyond's AI solution smashes four million hypotheses per minute

Solving problems quickly is a key goal for business. Now this AI solution reduced problem solving from months to minutes.
Written by Eileen Brown, Contributor

New York-based AI problem-solving platform SparkBeyond is an AI-powered research engine for predicting outcomes, discovering patterns, and taking advantage of data repositories across the web.

It generates millions of hypotheses about a problem by analyzing a company's owned data, along with publicly available data from the web such as news, maps, Wikipedia, and other sources.

To date, it has generated almost seven billion hypotheses, which have driven over $1 billion in impact for its clients.

Its tech is powered by a library of open-source algorithms across the world.

The software generates kinetic insights based on millions of hypotheses that adapt to changing data, conditions, and events.

It has announced that its platform can generate four million hypotheses per minute using its AI platform -- breaking new ground in AI impact generation.

This innovative AI start-up was founded in Israel in 2013, SparkBeyond's Problem Solving Platform draws from a network of data sources, and quantifies complex patterns to uncover new drivers never previously considered.

Organizations can gain operational intelligence to drive strategy. The problem-solving that it can do in minutes, some companies take a year or more to do.

Sergey Davidovich, CEO and co-founder of SparkBeyond, said: 

"At SparkBeyond, we believe that to find the right answer you must first ask the right question, but unfortunately humans can only ask so many questions at once. 

Therefore, we have developed a platform for AI-augmented problem-solving at a scale that empowers research analysts to accelerate breakthroughs.

By integrating the world's largest collection of algorithms, our AI technology is now capable of asking questions -- 4 million per minute and more than 7 billion total questions asked -- revealing key patterns within datasets in a fraction of the time it would take human operators."

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