Building Smarter Applications with Amazon Web AI Services

Amazon Q adds smart AI to Amazon Connect, AWS’s contact center tool. It spots customer issues during calls and gives agents quick, custom answers using data from your records and the web. This makes calls faster, improves service, and saves money with an AI helper.

Amazon Q adds smart AI to AWS Supply Chain, a cloud tool for managing supply chains. It checks your data to give useful insights and answers fast, saving time on searching and making the tool easier to use and fix.

Model customization lets you make AI special for your users. You tweak models with your data in a few easy steps. Amazon Bedrock gives you a private version of the model, and your data stays yours, not used to train the main one.

To give AI the latest company info, RAG grabs data from your files and mixes it into questions for better answers. Knowledge Bases for Amazon Bedrock does this automatically, customizing AI with your data without coding. It manages everything—grabbing data, finding answers, and showing where it came from. For messy data like text or pictures, it can sort and understand it using Bedrock tools or models. For organized data, it turns plain questions into database searches, getting info directly without extra steps.

Amazon Bedrock Agents use your company’s systems and data to handle tasks like answering customer questions or taking orders. You set them up quickly by picking a model and connecting it to your systems and APIs. The agent figures out what the user wants and does the job automatically. It’s secure, private, and doesn’t need manual setup or management.

Amazon Bedrock Data Automation makes it easy to get insights from things like documents, images, audio, and videos for AI apps and workflows. It can summarize videos, spot bad images, or analyze documents quickly and cheaply. You can tweak the results for your needs, and it includes tools to make the outputs more accurate and reliable.

Create your own machine learning (ML) models, including foundation models (FMs), to drive generative AI applications using built-in, specialized tools and efficient, affordable infrastructure.

Standardize and automate MLOps practices and governance across your organization to enhance transparency and ensure auditability.

Use human feedback throughout the ML process to boost the accuracy and relevance of foundation models (FMs) with human-in-the-loop features.

Receive support across the full ML development process, from preparing data and training models to deploying them. Amazon Q Developer provides code suggestions, answers your questions, and helps troubleshoot issues when errors arise.

With Amazon Polly, you can customize and control text-to-speech output to engage your audience. You can modify pronunciations of specific words using custom lexicons and adjust various aspects like emphasis, intonation, phrasing, and style with SSML tags. This flexibility helps you create voice AI outputs that perfectly match your business needs.

Amazon Polly provides cost-effective generative AI features, letting users convert text-to-speech with various voice options. It uses a powerful transformer to create voices in a smooth manner, producing synthetic speech that sounds assertive, engaging, and natural, like a real human voice.

Amazon Polly allows you to securely store and share your text-to-speech output in standard audio formats like MP3 and OGG, without extra costs. You can cache your files for faster access if needed. AWS ensures the security and privacy of your content, and it does not keep any of your text submissions.

Precisely identify legitimate and high-risk account registrations, enabling targeted implementation of additional verification measures, such as phone or email verification, to ensure account security.

Identify accounts that may misuse online services and set suitable limits on offer values to reduce risk.

Seamlessly integrate into the real-time account login process to identify compromised accounts while ensuring a smooth experience for legitimate users.