Building an Enterprise AI Assistant with Amazon Q Business - Internal Knowledge Search and Business Automation
Q Business connects data sources such as internal documents, Confluence, and SharePoint and achieves RAG-based enterprise search and question answering. This article explains how to build a secure foundation for making use of generative AI with guardrails and access control.
Overview of Q Business
Amazon Q Business is a generative AI assistant service that connects to data sources within an enterprise and lets employees ask questions, search, and summarize in natural language. It provides connectors for 40 or more data sources, including Confluence, SharePoint, Salesforce, ServiceNow, S3, and RDS, and lets you search knowledge scattered across the company in an integrated way. With its RAG (Retrieval-Augmented Generation) architecture, it searches the connected data sources for relevant information and generates answers based on that information, which reduces the risk of hallucinations (answers that differ from the facts). While Knowledge Bases for Bedrock is an API-based RAG foundation for developers, Q Business differs in that it lets you build an enterprise assistant with a web UI without code. In addition, Q Business stopped accepting new customers on July 30, 2026, and only existing customers can continue to use it (as of August 2026). For new deployments, Amazon Quick is suggested as an alternative with similar features.
Data Source Connections and Indexing
You create a Q Business application and ingest documents into the index with data source connectors. Connectors crawl data sources periodically and automatically synchronize new, updated, and deleted documents. You can choose the synchronization schedule from on-demand, hourly, daily, weekly, monthly, and custom. Document access control lists (ACLs) are automatically imported from the data sources, and users receive as search results only the information in documents they have access rights to. For example, because SharePoint permission settings are applied to Q Business, the risk of confidential documents leaking to users without permissions is reduced. With custom document enrichment, you can also use Lambda functions to add metadata and preprocess body text before indexing.
Guardrails and Access Control
Q Business integrates with IAM Identity Center to centrally manage user authentication and access control. Administrators can set guardrails for each topic to block questions about specific topics (performance reviews, salary information, and so on) and to restrict the data sources included in answers. In global controls, you manage settings that affect conversations across the entire application, such as whether to limit answers to internal data. With the plugin feature, you can execute actions such as creating Jira tickets, filing ServiceNow incidents, and updating Zendesk tickets through natural language instructions. In the administrator dashboard, you analyze users' question trends, feedback on the usefulness of answers, and usage for each data source, and make use of the results to improve the knowledge base.
Q Business Pricing
Q Business pricing consists of user licenses and index storage. Business Lite users cost approximately $3 per month and can use the basic features of question answering and search (as of August 2026). Business Pro users cost approximately $20 per month and can use action execution with plugins, custom application creation, and all features of the administrator dashboard. The index is charged by the number of units and time, and one unit includes capacity for up to 20,000 documents. Because a higher synchronization frequency for data source connectors increases connector usage time, you set data sources with a low update frequency to daily synchronization.
Use Cases and Business Efficiency
Q Business is an AI assistant for making use of knowledge scattered across the company in a cross-cutting way. It lets you search information divided among multiple document systems and data sources at once with natural language questions, and it answers while showing the evidence. It can streamline work in which people used to spend time searching, such as summarizing long documents, answering questions about regulations and manuals, and referring to past exchanges. Furthermore, if you use mechanisms that integrate with external systems, you can go beyond search and also request routine business operations. It reduces the time spent searching for information and allows employees to spend their time on their core work.
Permission-Aware Search and Maintaining Accuracy
AI that handles internal information needs a mechanism that returns only information within the scope that the user is originally allowed to access. Q Business controls search results based on the permissions set in the original systems so that information without permission does not get mixed into answers. This lets you make use of internal knowledge while reducing the risk of confidential information leaking. Because the accuracy of answers depends on the quality and upkeep of the connected data, you keep the connected information sources up to date and accurate. By observing usage, finding patterns of questions that cannot be answered well, and improving the data and settings, you can maintain practical accuracy.
Summary
Amazon Q Business is an enterprise generative AI assistant that integrates internal knowledge and lets you search, ask questions, and execute actions in natural language. It builds a secure knowledge search foundation with 40 or more data source connectors and ACL-based access control. Guardrails and plugins support safety and practicality, and the administrator dashboard drives continuous improvement.
References (Official AWS Resources)
The primary sources for this page are the official AWS website and documentation. Check the official pages below for the latest specifications and pricing.
- Amazon Q Business official page
- Amazon Bedrock official page
- Amazon S3 official page
- AWS Documentation (official)
If this page and the official documentation disagree, treat the official documentation as authoritative.