Bottlenose’s Nerve Center aims to automate business intelligence with AI

Bottlenose’s Nerve Center aims to automate business intelligence with AI

Bottlenose’s Nerve Center aims to automate business intelligence with AI

Los Angeles-based Big Data company Bottlenose Inc. has just revealed the next major iteration of its business intelligence product, Nerve Center, which uses a combination of real-time data mining, predictive analytics and machine learning to help companies make important decisions related to staffing, advertising, investments and more.

Advancements in machine learning and artificial intelligence seem to be arriving every day now, from Google’s landmark victory against a world champion Go player to Helsinki’s recent self-driving public transit tests, but Bottlenose CEO and co-founder Nova Spivack says that many powerful machine learning tools are still out of reach for mid-sized businesses, even if their costs are have dropped somewhat.

“Right now we’re in the middle of a transition from an earlier era, where these things were really expensive and you’d use Palantir or IBM to do huge projects, to an era where where a range of tools targeting CIOs, various Hadoop platforms and analytic platforms have all brought the price down by an order of magnitude for enterprise buyers.”

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Spivack said that with Nerve Center 3.0,f Bottlenose has managed to bring that price down even further, putting the cost of machine learning business intelligence into the tens-of-thousands rather than in the millions.

The latest iteration of Nerve Center offers a number of new and improved features over the previous version, most notably the ability to replicate many of the functions of a business analyst with machine learning.

Nerve Center draws on a firehose of over 2 million data sources, including social media, the dark web, bitcoin and more. With a minimal amount of training, businesses can learn how to leverage that data by building customized reports and alerts to do anything from measuring brand sentiment to predicting staffing needs or anticipating customer service issues.

“If you’re a person whose job it is to decide when and where to spend money on TV ad campaigns for beer brands,” Spivack offered as an example, “our system can advise you on what months, days or countries you should focus on and with what campaign.

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