AWS Machine Learning Blog

Category: Amazon Kendra

Deploying and using the Document Understanding Solution

Based on our day to day experience, the information we consume is entirely digital. We read the news on our mobile devices far more than we do from printed copy newspapers. Tickets for sporting events, music concerts, and airline travel are stored in apps on our phones. One could go weeks or longer without needing […]

Configuring your Amazon Kendra Confluence Server connector

Many builders and teams on AWS use Confluence as a way of collaborating and sharing information within their teams and across their organizations. These types of workspaces are rich with data and contain sets of knowledge and information that can be a great source of truth to answer organizational questions. Unfortunately, it isn’t always easy […]

Creating a multi-department enterprise search using custom attributes in Amazon Kendra

An enterprise typically houses multiple departments such as engineering, finance, legal, and marketing, creating a growing number of documents and content that employees need to access. Creating a search experience that intuitively delivers the right information according to an employee’s role, and the department is critical to driving productivity and ensuring security. Amazon Kendra is a […]

Getting started with the Amazon Kendra SharePoint Online connector

Amazon Kendra is a highly accurate and easy-to-use enterprise search service powered by machine learning (ML). To get started with Amazon Kendra, we offer data source connectors to get your documents easily ingested and indexed. This post describes how to use Amazon Kendra’s SharePoint Online connector. To allow the connector to access your SharePoint Online […]

Relevance tuning with Amazon Kendra

Amazon Kendra is a highly accurate and easy-to-use enterprise search service powered by machine learning (ML). As your users begin to perform searches using Amazon Kendra, you can fine-tune which search results they receive. For example, you might want to prioritize results from certain data sources that are more actively curated and therefore more authoritative. […]

How Citibot’s chatbot search engine uses AI to find more answers

Citibot is a technology company that builds AI-powered chat solutions for local governments such as Fort Worth, Texas; Charleston, South Carolina; and Arlington, Virginia. With Citibot, local residents can quickly get answers to city-related questions, report issues, and receive real-time alerts via text responses. To power these interactions, Citibot uses Amazon Lex, a service for building conversational interfaces for text and voice applications. Citibot built the chatbot to handle basic call queries, which allows government employees to allocate more time to higher-impact community actions.

Enhancing enterprise search with Amazon Kendra

Amazon Kendra is an easy-to-use enterprise search service that allows you to add search capabilities to your applications so end-users can easily find information stored in different data sources within your company. This could include invoices, business documents, technical manuals, sales reports, corporate glossaries, internal websites, and more. You can harvest this information from storage […]

Integrate Amazon Kendra and Amazon Lex using a search intent

Customer service conversations typically revolve around one or more topics and contain related questions. Answering these questions seamlessly is essential for a good conversational experience. For example, as part of a car rental reservation, you have queries such as, “What’s the charge for an additional driver?” or, “Do you have car seats for kids?” Starting […]