Preserving and taking advantage of institutional knowledge is critical for organizational success and adaptability. This collective wisdom, comprising insights and experiences accumulated by employees over time, often exists as tacit knowledge passed down informally. Formalizing and documenting this invaluable resource can help organizations maintain institutional memory, drive innovation, enhance decision-making processes, and accelerate onboarding for new employees. However, effectively capturing and documenting this knowledge presents significant challenges. Traditional methods, such as manual documentation or interviews, are often time-consuming, inconsistent, and prone to errors. Moreover, the most valuable knowledge frequently resides in the minds of seasoned employees, who may find it difficult to articulate or lack the time to document their expertise comprehensively.
This post introduces an innovative voice-based application workflow that harnesses the power of Amazon Bedrock, Amazon Transcribe, and React to systematically capture and document institutional knowledge through voice recordings from experienced staff members. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies such as AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. Our solution uses Amazon Transcribe for real-time speech-to-text conversion, enabling accurate and immediate documentation of spoken knowledge. We then use generative AI, powered by Amazon Bedrock, to analyze and summarize the transcribed content, extracting key insights and generating comprehensive documentation.
The front-end of our application is built using React, a popular JavaScript library for creating dynamic UIs. This React-based UI seamlessly integrates with Amazon Transcribe, providing users with a real-time transcription experience. As employees speak, they can observe their words converted to text in real-time, permitting immediate review and editing.
By combining the React front-end UI with Amazon Transcribe and Amazon Bedrock, we’ve created a comprehensive solution for capturing, processing, and preserving valuable institutional knowledge. This approach not only streamlines the documentation process but also enhances the quality and accessibility of the captured information, supporting operational excellence and fostering a culture of continuous learning and improvement within organizations.
This solution uses a combination of AWS services, including Amazon Transcribe, Amazon Bedrock, AWS Lambda, Amazon Simple Storage Service (Amazon S3), and Amazon CloudFront, to deliver real-time transcription and document generation. This solution uses a combination of cutting-edge technologies to create a seamless knowledge capture process:
This solution uses a custom authorization Lambda function with Amazon API Gateway instead of more comprehensive identity management solutions such as Amazon Cognito. This approach was chosen for several reasons:
Although this solution works well for this specific use case, it’s important to note that for production applications, especially those dealing with sensitive data or needing user-specific functionality, a more robust identity solution such as Amazon Cognito would typically be recommended.
The following diagram illustrates the architecture of our solution.
The workflow includes the following steps:
You need the following prerequisites:
us-east-1
AWS RegionThe AWS Cloud Development Kit (AWS CDK) is an open source software development framework for defining cloud infrastructure as code and provisioning it through AWS CloudFormation. Our AWS CDK stack deploys resources from the following AWS services:
To deploy the solution, complete the following steps:
README.md
file to set up your local environmentAs of this writing, this solution supports deployment to the us-east-1
Region. The CloudFront distribution in this solution is geo-restricted to the US and Canada by default. To change this configuration, refer to the react-app-deploy.ts GitHub repo.
npm install
to install the dependenciescdk deploy
to deploy the solutionThe deployment process typically takes 20–30 minutes. When the deployment is complete, CodeBuild will build and deploy the React application, which typically takes 2–3 minutes. After that, you can access the UI at the ReactAppUrl
URL that is output by the AWS CDK.
Our solution’s front-end is built using React, a popular JavaScript library for creating dynamic user interfaces. We integrate Amazon Transcribe streaming into our React application using the aws-sdk/client-transcribe-streaming
library. This integration enables real-time speech-to-text functionality, so users can observe their spoken words converted to text instantly.
The real-time transcription offers several benefits for knowledge capture:
In this solution, the Amazon Transcribe client is managed in a reusable React hook, useAudioTranscription.ts
. An additional React hook, useAudioProcessing.ts
, implements the necessary audio stream processing. Refer to the GitHub repo for more information. The following is a simplified code snippet demonstrating the Amazon Transcribe client integration:
For optimal results, we recommend using a good-quality microphone and speaking clearly. At the time of writing, the system supports major dialects of English, with plans to expand language support in future updates.
After deployment, open the ReactAppUrl
link (https://<cloud front domain name>.cloudfront.net
) in your browser (the solution supports Chrome, Firefox, Edge, Safari, and Brave browsers on Mac and Windows). A web UI opens, as shown in the following screenshot.
To use this application, complete the following steps:
The document generation process uses FMs from Amazon Bedrock to create a well-structured, professional document. The FM model performs the following actions:
The audio files and generated documents are stored in a dedicated S3 bucket, as shown in the following screenshot, with appropriate encryption and access controls in place.
To further enhance your knowledge capture solution and address specific use cases, consider the additional features and best practices discussed in this section.
For industries with specialized terminology, Amazon Transcribe offers a custom vocabulary feature. You can define industry-specific terms, acronyms, and phrases to improve transcription accuracy. To implement this, complete the following steps:
For handling large audio files or improving user experience, implement an asynchronous upload process:
For generating comprehensive documents covering multiple topics, refer to the following AWS Prescriptive Guidance pattern: Document institutional knowledge from voice inputs by using Amazon Bedrock and Amazon Transcribe. This pattern provides a scalable approach to combining multiple voice inputs into a single, coherent document.
Key benefits of this approach include:
The knowledge captured through this solution can serve as a valuable, searchable knowledge base for your organization. To maximize its utility, you can integrate with enterprise search solutions such as Amazon Bedrock Knowledge Bases to make the captured knowledge quickly discoverable. Additionally, you can set up regular review and update cycles to keep the knowledge base current and relevant.
When you’re done testing the solution, remove it from your AWS account to avoid future costs:
cdk destroy
to remove the solutionThis post demonstrates the power of combining AWS services such as Amazon Transcribe and Amazon Bedrock with popular front-end frameworks such as React to create a robust knowledge capture solution. By using real-time transcription and generative AI, organizations can efficiently document and preserve valuable institutional knowledge, fostering innovation, improving decision-making, and maintaining a competitive edge in dynamic business environments.
We encourage you to explore this solution further by deploying it in your own environment and adapting it to your organization’s specific needs. The source code and detailed instructions are available in our genai-knowledge-capture-webapp GitHub repository, providing a solid foundation for your knowledge capture initiatives.
By embracing this innovative approach to knowledge capture, organizations can unlock the full potential of their collective wisdom, driving continuous improvement and maintaining their competitive edge.
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