Amazon Quick as AI Assistant

Amazon Web Services continues to expand the possibilities for integrating generative AI into business applications. With Amazon Quick embedded chat, organizations can add an AI assistant directly to their web applications, enabling users to ask questions, explore data, and receive insights without switching to a separate service.

AWS also provides customization capabilities that allow organizations to align the embedded AI experience with their application's design, brand identity, and communication style.

An AI assistant within your application

Amazon Quick embedded chat provides a conversational AI interface that can be integrated directly into a web application.

Users can interact with AI in the same environment where they already work with business data. For example, an employee using a financial dashboard can ask questions about revenue, expenses, or margins and receive insights without leaving the application.

An important part of this integration is making the AI assistant feel like a native component of the application rather than an external tool.

Customizing the visual experience

Amazon Quick allows developers to adapt embedded chat to the application's existing design.

Developers can configure chat dimensions and positioning, CSS classes, borders, shadows, loading behavior, and other interface elements.

Because the chat itself is rendered inside an iframe, its internal elements cannot be styled directly with CSS. Instead, developers can style the surrounding container and use Amazon Quick Embedding SDK options to control the iframe's behavior and branding.

Default Amazon Quick brand attribution and the usage policy link can also be hidden when organizations want the chat experience to more closely match their application's design.

Creating a custom AI communication style

Visual consistency is only one part of the experience. The way the AI assistant communicates should also reflect the organization's context and communication style.

Amazon Quick allows organizations to create a custom chat agent and define persona instructions.

These instructions can determine the assistant's role, terminology, response structure, communication style, and knowledge boundaries.

For example, a financial AI assistant can be configured to lead with the key metric, provide a comparison with the previous period, use the organization's financial terminology, and limit its answers to relevant corporate data.

This makes it possible to build a specialized corporate AI assistant instead of providing users with a generic chatbot.

Additional capabilities for users

Using the SDK, developers can control which capabilities are available directly within the embedded chat.

These can include file attachments for analysis, web search, conversation history, and indicators showing the AI assistant's knowledge boundaries.

Developers can also configure an initial prompt that is automatically triggered when the chat opens. For example, an AI assistant embedded in a financial dashboard could immediately provide key financial highlights for the current quarter.

Connecting AI with application interactions

Amazon Quick Embedding SDK also enables programmatic interaction between application elements and the AI assistant through the sendPrompt() method.

This allows applications to automatically generate contextual AI requests based on user actions.

For example, when a user selects a Revenue metric in a dashboard, the application can automatically ask the AI assistant to explain the revenue trend and identify the factors driving it.

This approach connects traditional BI interfaces with conversational interaction, allowing users to explore business information more naturally.

Creating a consistent user experience

Amazon Quick embedded chat customization enables organizations to integrate generative AI into their products and internal applications without building a separate interface for the AI assistant.

The combination of visual customization, custom AI personas, and programmatic interaction through the SDK helps make conversational AI a natural part of the application experience.

As a result, users can explore data, receive insights, and interact with AI directly within their existing working environment.