At AWS re:Invent 2023, outgoing CEO Adam Selipsky introduced us to Amazon Q, an AI-powered assistant designed for its customers.
By connecting to enterprise data repositories, Amazon Q can logically summarise data, analyse trends, and facilitate dialogue about the information. It also answers questions across various business data, including company policies, product information, business results, code base, employee details, and more.
Recently, AWS announced the general availability of Amazon Q, which comes in three variations – Amazon Q for Developers, Amazon Q for Business and Amazon Q Apps.
Interestingly, Amazon Q fulfils similar functionalities as Microsoft Copilot or ChatGPT Enterprise but has more to offer.
Mai-Lan Tomsen Bukovec, the vice president of technology at AWS, believes Amazon Q is the most capable AI assistant because it builds on AWS’ data and development expertise of 18 years.
“Since Amazon Q for business can understand spoken and written language, it makes it easy for anyone to ask questions and get help with Q assistance. For example, instead of writing an SQL query to get data results, you can now simply ask your data question to Amazon Q for Business.
“Not only will Amazon Q for Business give you an answer, it will provide the response in a way that is easily understood – regardless of your degree of technical know-how,” Bukovec said in an exclusive interview with AIM.
Amazon Q vs Microsoft Copilot
Microsoft Copilot is integrated into Microsoft 365 apps like Word, PowerPoint, and Outlook to provide AI assistance across the full productivity suite. But what makes Amazon Q stand out, according to Bukovec, is its ability to integrate with your different identity.
“Now Identities can be an active directory or AWS Identity and Access Management (IAM) resources, and I think that’s incredibly important. Moreover, no other AI assistant has as many connectors to enterprise data sources – over 40 different enterprise data sources and growing.
“If you think about any organisation, in order to bring together information, you have to bring it together across all of these different sources. So, I would say that the first major point of differentiation is the ability to integrate across all the different data sources,” Bukovec said.
With Amazon Q, AWS customers can select from popular data sources and enterprise systems, including Amazon S3, ServiceNow, Slack, Google Drive, Microsoft SharePoint, and Salesforce, among others.
Lastly, what cannot be overstated is the security-oriented approach of enterprise AI. When developing enterprise AI, building from the ground up with a focus on security is essential. From the beginning, their strategy has involved never integrating customer data back into models.
“This enterprise AI mentality is embedded in every AWS service and all aspects of generative AI capabilities, including Amazon Q, both for business and developers. This foundational approach is unique and unparalleled in the industry,” Bukovec added.
A case for security and privacy
Since Amazon Q is built from the ground up for enterprise AI, security concepts like data privacy are integrated at its core. Bukovec affirms the same, saying that privacy and security are baked into every part of Amazon Q.
“It starts with the promise of never using customer data to train the underlying model and includes unique capabilities like the ability to use third-party identity providers so your data access continues to have the same controls and protections as it does today in your enterprise,” he said.
AWS is strongly emphasising on the security aspect of Amazon Q because, last year, the AI chatbot experienced severe hallucinations and leaked confidential data, including the location of AWS data centres, internal discount programmes, and unreleased features.
Amazon Q is AWS’ answer to Devin and GitHub Copilot Workspace
Interestingly, CodeWhisper, a competitor to Microsoft-owned Github Copilot, has been rebranded to Amazon Q for developers and packed with new features.
“The key takeaway about Amazon Q for Developers is that it encompasses the full spectrum of developer tasks beyond just code generation. While generating code is a critical component, developers often express that they spend less time on actual development than they would like.
“Being a developer involves research, analysing the coding environment, performing upgrades, and implementing security remediation. Q for Developers addresses this entire end-to-end workflow, making it a comprehensive tool for developers,” Bukovec said.
A similar tool currently under technical preview is GitHub Copilot Workspace, which is designed to help developers from idea conceptualisation to production throughout the software development lifecycle.
Similarly, Cognition Labs released Devin earlier this year, dubbed the world’s first AI software engineer.
While GitHub Copilot has already emerged as one of the most widely used AI tools by developers, the adoption of Amazon Q for Developers remains to be seen by AWS customers.
Models powering Amazon Q
While Microsoft Copilot is powered by OpenAI’s GPT models, Amazon Q, on the other hand, leverages a mixture of models available on AWS Bedrock, including Claude by Anthropic and Llama 3 by Meta, as well as models by Cohere.
“When you think of Amazon Q, it’s not just about the latest models and capabilities. It also incorporates advanced retrieval-augmented generation techniques and our custom approach to search techniques across data,” Bukovec concluded.
