Amazon Kendra Documentation

Amazon Kendra is an intelligent search service powered by machine learning. Kendra is designed to reimagine enterprise search for your websites and applications so your employees and customers can more easily find the content they are looking for, even when it’s scattered across multiple locations and content repositories within your organization.

Amazon Kendra uses machine learning to deliver more relevant answers from unstructured data. Search for general keywords like "health benefits" or ask natural language questions like "how long is maternity leave?” and Kendra is designed to use reading comprehension to give specific answers like "14 weeks”. For more general questions like "how do I configure my VPN?" Kendra is designed to give descriptive answers by extracting the most relevant text passage.

Amazon Kendra also supports FAQ matching and extracts answers from curated FAQs using a specialized model that is designed to pinpoint the closest question in the FAQ and return the corresponding answer. 

Incremental learning

Amazon Kendra is designed to continuously optimize search results based on end-user search patterns and feedback. For example, when users search “How do I change my health benefits?", multiple HR benefit documents will compete for a top spot. To determine the most relevant document for this question, Amazon Kendra is designed to learn from the user interactions and feedback to promote preferred documents to the top of the list. 

Tuning and accuracy

Customers can fine-tune search results and boost specific answers and documents in the results based on specific business objectives. For example, relevance tuning lets you boost results based on more authoritative data sources, authors, or document freshness.

To extend Amazon Kendra’s understanding of your specific business vocabulary, you can provide your own custom synonyms. Amazon Kendra uses these to automatically expand queries to include content and answers that match the extended vocabulary. 


To use connectors, you just add data sources to your Amazon Kendra index and select the connector type. Connectors can be scheduled to sync your index with your data source at predetermined intervals, so you search through up to date content. Amazon Kendra offers native connectors for many popular data sources, and in the event a native connector not available, Amazon Kendra offers a custom data source connector as well as a host of partner supported connectors. 

Domain optimization

Kendra uses deep learning models to better understand natural language queries and document content and structures for a wide range of internal use cases like HR, operations, support, and R&D. Kendra is also optimized to understand complex language from many different professional domains. 

Query autocompletion

Amazon Kendra includes the functionality to auto-complete an end user’s search query. Query auto-completion can help guide users towards more precise and commonly asked questions. More precise questions typically help result in more relevant and useful answers. For example, if you start typing "Where is" in the search box, Kendra can suggest options like "Where is the IT desk?", or "Where is the cafeteria?" and other related commonly asked questions, to complete the query.

Additional Information

For additional information about service controls, security features and functionalities, including, as applicable, information about storing, retrieving, modifying, restricting, and deleting data, please see This additional information does not form part of the Documentation for purposes of the AWS Customer Agreement available at, or other agreement between you and AWS governing your use of AWS’s services.