PureDiscovery, a Dallas-based big data startup, thinks it has the has the answer to outdated enterprise search technology, and it’s called BrainSpace. The company claims BrainSpace can learn just about everything about how pieces of content are related to one another. That means users will become less dependent on searching for information because the platform will feed them what they want to know as they interact with other content.
One could characterize BrainSpace as just another semantic search technology, PureDiscovery CEO Dave Copps told me, but they’d be wrong. Whereas many of those technologies rely on linking together pieces of data within specific indexes using industry-specific ontologies, BrainSpace doesn’t try to index data at all. It cares that documents contain similar words or concepts, but it also cares about a lot more.
It’s trying to create something more like a semantic brain. “We want to create an environment that understands people’s interests in a deep, deep, deep way,” he said. That requires learning new connections for each new company and situation.
In practice, Copps explained, BrainSpace currently acts as a sort of “purgatory” in between a user’s query and any number of separate indexes. BrainSpace reads the query, analyzes it against what it has learned about the data, and then pulls the relevant information. The technology already has proven itself in the world of archived content — it powers semantic search across more than 350 million documents for LexisNexis’s services and is rather popular in the world of legal e-discovery.
But PureDiscovery has its eyes on even bigger fish, which is why it calls itself the first “post-search company.”
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