Amazon Kendra is a highly accurate and easy to use enterprise search service that’s powered by machine learning. Kendra delivers powerful natural language search capabilities to websites and applications so end users can more easily find the information they need within the vast amount of content spread across the company.
Kendra understands natural language questions to get the answers, whether that is a precise answer, an FAQ, or an entire document. It lets easily add content from file systems, SharePoint, intranet sites, file sharing services, and more, into a centralized location in order to allow quickly search all of the information to find the best answer.
Results get better over time search, because Kendra’s machine learning algorithms learn which results your users find most valuable. Kendra actively retrains deep learning models built for data set and employee usage patterns to improve search accuracy. As end-users interact with search results, Kendra fine tunes its results. Kendra gives the option to manually tune relevance; it is possible to boost certain fields in index like document freshness, view counts, or specific data sources.
Kendra helps ensure that search results adhere to existing access policies by scanning permissions on documents so that results only contain documents the user has permission to access, and it encrypts data in transit and at rest.
Kendra’s models are optimized to understand language from domains like IT, health care, and insurance, plus energy, industrial, financial services, legal, media and entertainment, travel and hospitality, human resources, news, telecommunications, mining, food and beverage, and automotive. Beyond Kendra, Microsoft has launched Project Cortex, a service that taps AI to automatically classify and analyze an organization’s documents, conversations, meetings, and videos. It was in some ways a direct response to Google Cloud Search, which pulls in data from a range of third-party products and services running both on-premises and in the cloud, relying on machine learning to deliver query suggestions and surface the most relevant results.
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