Democratizing institutional knowledge: Building an AI-powered knowledge management system with AWS
Learn how to build a customizable, smart-caching knowledge management system on AWS that captures and delivers institutional (tribal) knowledge through a voice-first AI avatar. The accelerator uses Amazon Bedrock Knowledge Bases for retrieval-augmented generation and deploys in hours with AWS CloudFormation.
Many companies face challenges preserving institutional knowledge, leading to knowledge gaps that affect productivity and innovation. Traditional documentation methods are often inadequate, resulting in outdated or difficult-to-access information when required. AWS offers a customizable, intelligent avatar-based knowledge management system to address these issues. This solution captures, maintains, and delivers critical knowledge across various industries.
Manufacturing firms can capture production procedures and maintenance protocols before seasoned technicians retire. Healthcare facilities, financial services firms, energy companies, and government agencies can also utilize this system to preserve their unique knowledge bases. Knowledge workers can access procedures and policies through natural language queries, while experts and retiring employees can upload documentation to pass on their expertise.
The system is deployed via a browser-based interface supporting text and voice interactions, enabling hands-free operation. Users engage with the AI-powered avatar as if conversing with a colleague. This voice-first, avatar-driven interaction simplifies adoption for non-technical users, reducing the learning curve compared to text-based chat interfaces.
Behind the scenes, Amazon Cognito handles access management, while Amazon API Gateway controls access to the system's components. The knowledge-processing core employs Amazon Bedrock Knowledge Bases, utilizing Amazon S3 as the data source for institutional knowledge. Amazon Bedrock handles chunking, embedding, and retrieval, grounding each answer in your own documents. The Amazon OpenSearch Serverless vector store acts as the knowledge base, while AWS Lambda functions manage the workflow.
A built-in DynamoDB cache reuses previous answers for repeated questions, reducing variable AI inference costs. At deployment, an Amazon OpenSearch Serverless vector store is created in your account and incurs a standing baseline cost, approximately a few hundred USD per month at default settings, which remains independent of query volume.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
