待翻译：Democratizing institutional knowledge: Building an AI-powered knowledge management system with AWS

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Organizations across industries struggle with managing institutional knowledge, the collective wisdom and experience accumulated over years of operations. This “tribal knowledge” often disappears when key personnel leave, creating knowledge gaps that impact efficiency and innovation. Traditional documentation methods have proven inadequate, often resulting in outdated or inaccessible information when it’s needed most. In this post, we present a customizable, smart-caching cloud-based solution that captures, maintains, and delivers institutional knowledge through an intelligent avatar system, powered by AWS services. Who should use this solution Organizations across diverse industries can use this system to preserve critical knowledge. For example, manufacturing organizations can capture production procedures and maintenance protocols before experienced technicians retire. Other organizations like healthcare facilities, financial services firms, energy companies, and government agencies can also tailor their use cases with the solution. Knowledge workers can access procedures and policies through natural language queries instead of searching multiple repositories, while subject matter experts and retiring employees can upload documentation to preserve their expertise for future generations. Organizations can deploy the system with desktop browser access for detailed research, voice interaction for hands-free operation, and text-based queries for quick reference adapting to the specific operational contexts of each industry. Solution overview Our solution uses AWS services to create a scalable, configurable knowledge management system that you can adapt to your organization’s needs. At its core, the architecture combines advanced AI capabilities with robust cloud infrastructure to deliver an intuitive, responsive knowledge delivery system. The foundation of our solution is a browser-based interface that supports both text and voice interactions, making knowledge accessible through natural conversation. This interface connects to a configurable avatar system that can work with any AI avatar solution, providing organizations with the flexibility to choose or change their preferred avatar technology. Figure 1: Solution architecture for the knowledge management system Behind the scenes, Amazon Cognito secures access management, while Amazon API Gateway provides controlled, monitored access to the system’s components. The knowledge-processing core uses Amazon Bedrock Knowledge Bases for managed Retrieval Augmented Generation (RAG): institutional knowledge stored in Amazon Simple Storage Service (Amazon S3) is the data source, and Amazon Bedrock handles chunking, embedding (using Amazon Titan Text Embeddings), and retrieval, grounding each answer in your own documents. The knowledge base is backed by an Amazon OpenSearch Serverless vector store. Amazon DynamoDB provides response caching, and AWS Lambda functions orchestrate the workflow. Cost note: The Amazon OpenSearch Serverless vector store is created in your account by the deployment and bills per OpenSearch Compute Unit (OCU) with an always-on minimum, independent of query volume. Treat this as a standing baseline cost (on the order of a few hundred USD per month at the default floor) when budgeting. It is the largest fixed component of the solution’s cost. See Cost optimization through smart caching for how caching reduces the variable inference cost on top of this baseline. Why this solution? Organizations seeking to preserve institutional knowledge have several approaches available, from building custom solutions on Amazon Bedrock to deploying text-based chat agents. This solution differentiates through three core capabilities. Voice-first, avatar-driven delivery: This solution is built around voice interaction with an AI-powered avatar rather than a text-only chat interface. Non-technical end users need little to no learning curve. They speak to the avatar and receive spoken answers, the same way they would ask a colleague. Workers in connected settings such as training rooms, control rooms, quality labs, and maintenance-planning offices can query the system hands-free while reviewing procedures. Avatar-based interaction can also increase adoption and trust among non-technical populations compared to text-only chatbots. (This is a cloud-connected design. See the connectivity requirement under Scalability and performance characteristics.) Simplicity for knowledge owners: Content management requires no technical expertise. Knowledge owners simply upload existing documents into Amazon S3 (Word, PDF, plain text, Markdown, or JSON). An ingestion sync then chunks and embeds each document into the vector store, after which it becomes queryable. New documents are queryable shortly after upload, once that automated sync completes. It is not instantaneous. There is no need to restructure content, tag metadata, or hand-build a retrieval pipeline. Rapid deployment with built-in cost optimization: The full prototype deploys in hours through AWS CloudFormation. A built-in DynamoDB cache reuses previous answers for repeated questions, reducing variable AI inference cost. In our testing, cache hit rates of 50–70 percent were achievable for workloads dominated by repeated questions. Actual savings depend on how repetitive your query mix is. How this compares to alternatives Building a custom solution on Amazon Bedrock provides full architectural control but requires independently designing and integrating voice processing, avatar rendering, caching, and retrieval pipelines. This typically takes weeks to months and assumes deep technical expertise across multiple AWS services. Text-based chat agents such as Amazon Q and custom Amazon Bedrock chat interfaces offer a faster path to deployment and excel at typed knowledge retrieval. This solution adds voice-first interaction and avatar engagement on top, which improves adoption among frontline workers who benefit from hands-free access. This solution fills the gap: a production-quality accelerator deployable in hours, voice-first with visual avatar engagement, and cost-optimized through smart caching. For organizations that need institutional knowledge in the hands of non-technical workers quickly and affordably, this accelerator alleviates the complexity of assembling these capabilities independently. When to choose this solution Choose this accelerator when your organization needs to: Deliver institutional knowledge to non-technical or frontline workers through voice and visual interaction. Deploy quickly with a proven prototype rather than building from scratch. Reduce AI inference costs through intelligent caching. Preserve knowledge from retiring experts with minimal friction. Implementation journey The implementation journey consists of three main phases: knowledge foundation setup, infrastructure deployment, and AI integration. Each phase builds upon the previous one to create a comprehensive knowledge management system. Phase 1: Knowledge foundation setup. The implementation process begins with organizing institutional knowledge. Documents are uploaded to the Amazon S3 knowledge repository, and Amazon Bedrock Knowledge Bases ingests them from there. If your source content lives in other systems or formats, you can optionally add an AWS Glue ETL job to convert it into an AI-optimized format before uploading. This ETL step is a separate, optional integration and is not created by the CloudFormation deployment. Organizations whose documents are already in the supported formats listed below can upload directly to S3. Key considerations: Supported formats: Documents can be in various formats (Word, PDF, plain text, Markdown, JSON). Optimal output: Markdown (.md) or structured JSON are recommended for best AI retrieval performance. ETL handles the conversion: If you use the optional AWS Glue ETL job, it transforms source documents into the recommended output format before they are uploaded to S3. For detailed guidance on using AWS Glue for data extraction and transformation, refer to the AWS Glue ETL documentation. Phase 2: Infrastructure deployment. The core infrastructure deployment begins with AWS CloudFormation templates, facilitating consistent and repeatable deployments. This phase includes: Setting up Amazon Cognito for secure user authentication and access controls. Configuring API Gateway endpoints to manage component communication. Establishing S3-based repositories for knowledge storage. Implementing DynamoDB for response caching. Phase 3: AI integration. The final phase integrates the AI capabilities and processing pipeline: Configuring Amazon Bedrock for knowledge processing and query understanding. Setting up Lambda functions for request orchestration and response handling. Implementing the audio processing pipeline: Amazon Transcribe for converting voice input to text. Amazon Polly for converting text responses to natural speech. Integrating the chosen avatar system for human-like interactions. The system maintains high performance through an intelligent caching algorithm that significantly reduces response times while preserving information accuracy. The combination of these components creates an experience where you can interact with the knowledge base through your preferred method of communication, whether text or voice. Implementation steps: This guide outlines the steps to deploy the Auto Demo application. The steps that follow are written and tested for us-east-1, which we chose because it has the broadest availability of the foundation models (FMs) and avatar streaming used here. If you deploy in another AWS Region, confirm that your chosen Amazon Bedrock models and the avatar provider are available there, and update the hardcoded Region in the source, before you begin. Prerequisites Initial deployment requires technical capabilities from IT or DevOps teams for one-time setup, including basic AWS Management Console navigation, infrastructure-as-code understanding, and identity management familiarity. End users require no technical capabilities. The conversational interface alleviates barriers. Users simply access a URL and interact naturally through text or voice as they would with a human expert. AWS account Access to an AWS account with permissions to create S3 buckets, CloudFormation stacks, and invoke Amazon Bedrock models. Appropriate AWS Identity and Access Management (IAM) permissions for deploying and managing AWS resources. Required IAM permissions by service The CloudFormation stack creates and manages resources across multiple AWS services. To deploy and operate this solution, the deploying IAM role or user requires permissions for the following services: Amazon Bedrock (foundation model invocation and Amazon Bedrock Knowledge Bases retrieval) Amazon S3 (knowledge base storage and application files) AWS Lambda (serverless compute for request orchestration) Amazon Cognito (user authentication and access management) API Gateway (HTTP endpoint management) Amazon OpenSearch Serverless (vector store backing the Amazon Bedrock knowledge base) Amazon Polly (text-to-speech for voice responses) Amazon Transcribe (speech-to-text for voice input) Amazon CloudWatch Logs (centralized logging) IAM PassRole (service-role delegation for the Lambda and knowledge-base execution roles) Note: For production deployments, follow the principle of least privilege by scoping resource ARNs to your specific account and Region. The CloudFormation stack automatically creates the necessary execution roles with appropriately scoped permissions for runtime operations. Foundation model access: This solution uses Amazon Titan Text Embeddings (amazon.titan-embed-text-v1) to embed documents into the vector store, and Amazon Nova Pro (amazon.nova-pro-v1:0) and Anthropic Claude 3 Sonnet (anthro [truncated for AI cost control]