Amazon Kendra Features
Intelligent Search
Amazon Kendra employs machine learning to provide more meaningful replies from unstructured data. Amazon Kendra will employ reading comprehension to provide specific answers when you search for general terms (such as "health benefits") or ask natural language queries ("How long is maternity leave?"). For more general inquiries, such as "How do I set up my VPN?" Amazon Kendra provides descriptive responses by analyzing the most relevant text passage.
Amazon Kendra employs a deep learning semantic search engine for a reliable content rating in addition to extracted responses and FAQ matching. Overall, this results in a richer search experience that gives particular answers as well as related stuff to explore if you require additional information.
Incremental Learning
Amazon Kendra employs machine learning to continuously improve search results based on end-user search trends and feedback. For instance, when consumers search for "How can I alter my health benefits?" Several human resources (HR) benefit documents will fight for the top slot. Amazon Kendra will learn from user interactions and input to promote favored documents to the front of the list in order to determine the most relevant document for this topic. Amazon Kendra employs incremental learning approaches automatically, requiring no ML experience.
Tuning and Accuracy
Based on specific business objectives, you can fine-tune search results and promote specific answers and documents in the results. For example, Relevance tuning, allows you to increase results based on more trustworthy authors, data sources, or document freshness.
You can add your own synonyms to Amazon Kendra for understanding of your specific business terminology. These are used by Amazon Kendra to automatically expand searches to include content and responses that match the extended vocabulary. For example, when a user inquires, "What is an HSA?" Amazon Kendra would return documents with the words "Health Savings Account" or "HSA" in them.
Connectors
Connectors are simple to use, add data sources to your Amazon Kendra index and choose the connectivity type. Connectors can be configured to automatically sync your index with your data source, ensuring that you're always browsing through the most recent content. Amazon Kendra has native connectors for a variety of common data sources, including Amazon Simple Storage Service (S3), Microsoft ServiceNow, Google Drive, SharePoint, Salesforce, Confluence, and many more. If a native connector is unavailable, Amazon Kendra provides a custom data source connector as well as a number of partner-supported connectors.
Domain Optimization
Amazon Kendra uses deep learning models to comprehend natural language queries and document content and structures for a variety of internal use cases, including HR, operations, support, and research and development. Amazon Kendra is also designed to understand the complicated language from industries including IT, finance, pharmaceuticals, insurance, oil and gas, industrial manufacturing, legal, travel and hospitality, media and entertainment, health, news, telecommunications, food and beverage, mining, and automotive. To receive the most accurate answer, a user searching for HR answers may input "deadline for submitting HSA form," and Amazon Kendra would also search for "deadline for filing health savings account form.
Experience Builder
With Amazon Kendra, you can now launch a fully working and configurable search experience in a few minutes, without any coding or machine learning knowledge. Experience Builder provides a simple visual approach for swiftly creating, customizing, and launching your Amazon Kendra-powered search application in the cloud. Start with the builder's ready-to-use search experience template, which can be changed by dragging and dropping the components you desire, such as filters or sorting. When you are ready to deploy the experience, you can invite others to participate or test your search application for feedback, and then share the project with all users.
Search Analytics Dashboard
The Amazon Kendra Search Analytics Dashboard provides you with a deeper understanding of quality and usability metrics across your Amazon Kendra-powered search applications. The Analytics Dashboard assists administrators and content developers in understanding how quickly end users locate relevant search results, the quality of the search results, and content gaps. The Amazon Kendra Search Analytics Dashboard shows how your users interact with your search application and how successful your search results are. The analytics data can be viewed in the console via a visual dashboard, or you can create your own dashboards by accessing the Search Analytics data via an API.
Custom Document Enrichment
You can use Amazon Kendra Custom Document Enrichment to create a custom ingestion pipeline that can pre-process documents before they are indexed in Amazon Kendra. For example, you can enrich documents with additional metadata, convert scanned documents to text, classify documents, extract entities, and further alter the document using custom ETL procedures while consuming content from a source like SharePoint using our connections. Enrichment is carried out using simple rules that may be defined via the console or by running AWS Lambda functions. Other AWS AI Services, such as Amazon Comprehend, Amazon Transcribe, or Amazon Textract, can be called using these functions if desired.
Query autocompletion
Amazon Kendra has the ability to autocomplete a user's search input. Query autocompletion not only saves the user roughly 25% of their typing time, but it also guides them to more precise and often asked inquiries. These inquiries usually provide more relevant and meaningful answers. For example, if you begin typing "Where is" in the search field, Amazon Kendra will suggest alternatives to complete the inquiry, such as "Where is the IT desk?" or "Where is the cafeteria?" and other related popular inquiries.
Use Cases of Amazon Kendra
Accelerated Research and Development
Scientists and developers in charge of new research and development want to access data from previous projects that are buried deep within their corporate data stores. They spend less time looking and more time innovating with faster, more accurate searches.
Minimize Regulatory and Compliance Risks
To improve policy enforcement and compliance processes, use machine learning to swiftly detect and comprehend regulatory policies published across hundreds of websites.
Improve Customer Interactions
Amazon Kendra better understands what your customers are asking and gives more relevant responses and intuitive experiences, whether through Q&A chatbots, agent-assist, or consumer web search.
Increase Employee Productivity
Enterprises can establish and maintain a single dynamic knowledge catalog for all employees by unifying and indexing content from diverse, fragmented, and multi-structure information silos across the organization. Users may rapidly search and retrieve the most relevant information from any knowledge source using this unified view, allowing them to make more informed decisions.
Learn more, Amazon Hirepro
FAQs
What is Amazon Textract?
Amazon Textract is an ML service that extracts text, handwriting, and data from scanned documents automatically. To recognize, understand, and extract data from forms and tables, it goes beyond simple optical character recognition (OCR).
Is Amazon Kendra serverless?
Kendra is serverless, so application owners don't have to manage the underlying infrastructure that performs searches. However, they might have to manage data sources, depending on where data is stored.
What is an index in Amazon Kendra?
An index holds the contents of your documents and is structured in a way to make the documents searchable. The way you add documents to the index depends on how you store your documents.
Conclusion
In this article, we have discussed the following topics:
- Benefits of Amazon Kendra
- Features of Amazon Kendra
- Use Cases of Amazon Kendra
Want to learn more about Data Analysis? Here is an excellent course that can guide you in learning.
Happy Coding!
24+ registered 
