Quadra at Work and solution design

Quadra as an Advanced AWS Consulting Partner, engaged with Customer to address their business needs and current challenges. We designed a comprehensive solution using AWS services to develop a robust virtual assistant for managing their diverse data types, including manufacturing data, SME data, email conversations, service letters, field inspection reports, maintenance records, and equipment manufacturing data. This virtual assistant aims to streamline data management, enhance operational efficiency, and improve data-driven decision-making.

• Amazon Bedrock and Bedrock Guardrails: Utilized the Amazon Titan V2 Embedding model and Claude Sonnet 3.5 Model for embeddings and prompting, with guardrails in place to ensure safe and efficient AI-driven delivery.
• Amazon S3: Used for secure and scalable storage of documents and datasets.
• Amazon OpenSearch Serverless: Utilized for efficient searching and indexing of embedded text documents, enabling quick retrieval of information through Retrieval-Augmented Generation (RAG) techniques.
• Amazon Lambda: Implemented for running serverless functions to process data and automate workflows as part of the virtual assistant’s operations.
• Amazon API Gateway: Provided a secure and scalable entry point for applications to interact with backend Lambda functions, facilitating the integration of GenAI processing capabilities into Manufacturing Knowledge assistant application.
• VPC Private Link: Utilized to establish secure and private connectivity between customer network and AWS services. By leveraging VPC Private Link, customer team ensured that all traffic to AWS services remains within the AWS network, minimizing exposure to the public internet and enhancing security.
• Amazon Cloud trial: Enabled for logging and monitoring of API calls to ensure compliance and provide insights into user activity.
• Amazon CloudWatch: Utilized for monitoring the virtual assistant, capturing logs, and setting up alarms.
• Amazon SNS: Configured for generating alerts in case of function failures or critical events, ensuring timely notifications.

Quantifiable Improvements

• Enhanced Operational Efficiency and Quick Issue Resolution: By reducing manual data retrieval by 70%, customer Engineering team can swiftly access technical information and process flows, cutting issue resolution time and improving overall efficiency thereby easing staff workload and minimizing delays across assembly lines.
• Reduced Dependency on Expertise: Operations team can work more efficiently and focus on strategic tasks, reducing the increased workload on staff and dependency on experts for issue resolution.
• Improved Accuracy: Leveraged RAG techniques to enhance search precision and relevance, boosting the accuracy of retrieved information by 30%.
• LLM as a Service: Utilized Bedrock services with enabled guardrails to ensure responsible AI, featuring seamless API integration and a cost-effective usage-based model.
• Cost Optimization: Efficient resource use and automation slash operational costs by 50%, tackling inefficiencies.