Improving bot accuracy in Amazon Lex starts with handling how customers communicate naturally. Your customers express the same request in dozens of different ways, combine multiple pieces of information in one sentence, and often speak ambiguously. The Assisted NLU (natural language understanding) feature in Amazon Lex helps you improve bot accuracy by handling these natural …
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This post was co-authored with Neevash Ramdial, Technical Marketing leader at Stream Building production-grade voice agents that feel natural and responsive is a complex engineering challenge. You must orchestrate speech-to-speech models, manage low-latency audio streaming, and handle connection lifecycle. You also need to deliver consistent experiences across web, mobile, and desktop applications. In this post, …
Real-time voice agents with Stream Vision Agents and Amazon Nova 2 Sonic Read More »
Amazon Quick is an AI-powered unified intelligence service that brings together an organization’s data, structured data and unstructured enterprise content like documents, emails, and knowledge bases into a single service where anyone can explore, analyze, and take action. With over 40 application integrations, Quick bridges the last-mile gap between insights and action so users can …
From siloed data to unified insights: Cross-account Athena Access for Amazon Quick Read More »
AI agents with unrestricted web access pose significant security risks. Without Chrome enterprise policies to control browser behavior, an agent might navigate to unauthorized domains, store credentials in the browser’s password manager, or download files outside approved workflows. Organizations with internal services that use a private certificate authority (CA) face an additional barrier. Every HTTPS …
Financial institutions process thousands of complex documents daily. Optical Character Recognition (OCR) errors in financial data can propagate through interconnected calculations, affecting analytical accuracy. While a single OCR error in a standard legal document might require only a quick manual correction, the same mistake in financial data can cascade through interconnected calculations, leading to systematic …
Build financial document processing with Pulse AI and Amazon Bedrock Read More »
Building end-to-end live streaming applications with real-time voice interaction presents several challenges: network bandwidth constraints can cause high latency and quality degradation in time-critical applications. Language barriers limit effective human-machine interaction in multilingual voice communication. Scalability and resilience require a difficult balance between performance and infrastructure costs. Cross-browser and mobile compatibility demands significant development effort, …
Build real-time voice streaming applications with Amazon Nova Sonic and WebRTC Read More »
Model Context Protocol (MCP) adoption has accelerated rapidly since its introduction in November 2024. Enterprises now manage dozens to hundreds of MCP servers—tools that extend AI agent capabilities by connecting them to external data sources and APIs. The Agent-to-Agent (A2A) Protocol followed in April 2025, enabling autonomous agents to communicate directly without human intervention. More …
Securing AI agents: How AWS and Cisco AI Defense scale MCP and A2A deployments Read More »
When you fine-tune large language models (LLMs) with Amazon SageMaker AI while using Databricks Unity Catalog, you might face unique challenges like how to maintain strict data governance while using best-in-class machine learning (ML) services. Unity Catalog governs metadata and permissions, while the underlying data resides in Amazon Simple Storage Service (Amazon S3) when you …
Fine-tune LLM with Databricks Unity Catalog and Amazon SageMaker AI Read More »
Amazon’s Finance Technology (FinTech) teams build and operate systems for Amazon teams to manage regulatory inquiries in compliance with different jurisdictions. These teams process regulatory inquiries from authorities, each presenting different requirements, document formats, and complexity levels. Processing these regulatory inquiries involves reviewing documentation, extracting relevant information, retrieving supporting data from multiple systems within Amazon’s …
How Amazon Finance streamlines regulatory inquiries by using generative AI on AWS Read More »
Before you can extract information from documents using intelligent document processing (IDP) techniques, you need a schema for each document class that defines what to extract. But how do you create schemas when you have thousands of documents and don’t know what classes exist? Doing this at scale can take substantial manual effort, making downstream …
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