May 25, 2024
Amazon unveils Q, an AI-powered chatbot for businesses


Amazon is launching an AI-powered chatbot for AWS customers called Q.

Unveiled during a keynote at Amazon’s re:Invent conference in Las Vegas this morning, Q — starting at $20 per user per year — can answer questions like “how do I build a web application using AWS?” Trained on 17 years’ worth of AWS knowledge, Q will offer a list of potential solutions along with reasons you might consider its proposals.

“You can easily chat, generate content and take actions [with Q],” AWS CEO Adam Selipsky said onstage. “It’s all informed by an understanding of your systems, your data repositories and your operations.”

AWS customers configure Q by connecting it to — and customizing it with — organization-specific apps and software like Salesforce, Gmail and Amazon S3 storage instances. Q indexes all connected data and content, “learning” aspects about a business including its organizational structures, core concepts and product names.

From a web app, a company can ask Q to analyze, for example, which product features its customers are struggling with and possible ways to improve them — or, a la ChatGPT, upload a file (a Word doc, PDF, spreadsheet and the like) and ask questions about that file. Q draws on its connections, integrations and data, including business-specific data, to come up with responses along with citations.

Q goes beyond simply answering questions. The assistant can take actions on a user’s behalf through a set of configurable plugins, like automatically creating service tickets, notifying particular teams in Slack and updating dashboards in ServiceNow. To prevent mistakes, the chatbot has users inspect actions it’s about to take before they run and link to the results for validation.

Accessible from the AWS Management Console and the aforementioned web app, as well as existing chat apps like Slack, Q has a thorough understanding of AWS and the products and services available through it, as you might imagine. Amazon says that Q can understand the nuances of app workloads on AWS, suggesting AWS solutions for apps that only run for a few seconds versus minutes or hours or apps that only very infrequently access storage, for instance.

Onstage, Selipsky gave the example of an app that relies on high-performance video encoding and transcoding. Asked about the best EC2 instance for the app in question, Q would give a list taking into account performance and cost considerations, Selipsky said. 

Q can also troubleshoot things like network connectivity issues, analyzing network configurations to provide remediation steps.

And Q ties in with CodeWhisperer, Amazon’s service that can generate and interpret app code. Within a supported IDE (e.g. Amazon’s CodeCatalyst), Q can generate tests to benchmark software drawing on knowledge of a customer’s code. Q can also create a draft plan for implementing new features in software or transforming code and upgrading code packages, repositories and frameworks — plans that can then be refined and executed using natural language.

Selipsky says that Amazon used Q internally to upgrade around 1,000 apps from Java 8 to Java 17 — and test those apps — in just two days.

Amazon says that it’s also building Q its first-party products like QuickSight, a business analytics service. Q within QuickSight can provide visualization options for business reports, automatically reformatting them, or answer questions about data in a report.

Q is also making its way into Amazon’s contact center software, Amazon Connect. Now — powered by Q — customer service agents can get proposed responses to customer questions with suggested actions and links to related support articles without having to type those customer questions in a text bar. Q also generates a post-call summary supervisors can use to track follow-up steps.

Selipsky emphasized several times throughout the keynote that the answers Q gives — and the actions it takes — are fully controllable and filterable. Q will only return info a user’s authorized to see, and admins can restrict sensitive topics, having Q filter out inappropriate questions and answers where necessary.

In addition, Q models — a mix of models from Bedrock, Amazon’s AI dev platform, including Amazon’s own in-house Titan family — don’t train on a customer’s data, Selipsky said.

In many ways, Q seems like Amazon’s answer to Microsoft’s Copilot for Azure, which is was in turn Microsoft’s answer to Duet AI in Google Cloud. Both Copilot for Azure and Duet AI in Google Cloud take the form of a chat-driven assistant for cloud customers, suggesting configurations for apps and environments and helping with troubleshooting by identifying potential issues — and solutions.

But Q seems to be a bit more comprehensive — touching on a wide range of business intelligence, programming and configuration use cases. Ray Wang, founder the principal analyst at Constellation Research, believes that it’s the “most important” announcement at re:Invent so far.

“It’s about arming developers with AI so that they’re successful,” he said in a statement.

We’ll just have to see if it works as well as Amazon says that it does.



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