Kendra, which utilizes machine learning, will be useful for enterprises looking to index internal data sources and make them searchable. Users can deploy language queries and Kendra returns the most relevant results in the form of a precise answer or document.
Swami Sivasubramanian, vice president machine learning at AWS, said in a statement, “Our customers often tell us that search in their organizations is difficult to implement, slows down productivity, and frequently doesn’t work because their data is scattered across many silos in many formats.”
Sivasubramanian claimed Kendra would empower employees at organizations “with highly accurate, machine learning-powered enterprise search, which makes it easier for them to find the answers they seek across the full wealth of an organization’s data.”
According to AWS, enterprise search is difficult to implement, as the underlying data is unstructured and spread across different data sources. Search is usually done through keywords and not natural language queries, and these keywords can be spread across multiple documents in differing contexts generating random results.
AWS’s Kendra is industry-specific, and its usability spans IT, healthcare, and insurance. Industrial, energy, financial services, legal, media and entertainment, travel and hospitality, human resources, news, telecommunications, mining, food, and beverage and automotive will be added in the second half of this year.
The target of enterprise search has been to operate like Alphabet’s Google, but AWS’s Kendra would give any company operating in a supported industry a head start in search because the search engine would understand language specific to the industry. Kendra search boxes can be inserted into websites or applications and would support features like typeahead.
The addition of AI and machine learning would boost enterprise search significantly, reported Techcrunch.
On Monday, Amazon shares closed 1.24% higher at $2,409.
This story originally appeared on Benzinga.
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