AllCode's Secure Retrieval-Augmented Generation (RAG) Pipeline on AWS

Our expert team will build and deploy a production-grade RAG pipeline tailored to your use case and hosted entirely within your AWS environment. The pipeline will be designed with scalability, modularity, and security in mind, using industry-leading tools and AWS services:

• Document Storage & Indexing with Amazon S3: All unstructured documents are securely stored in Amazon S3, serving as the central repository for knowledge ingestion and indexing.
• Embeddings and Vector Search via Amazon Bedrock Knowledge Base: We leverage Amazon Bedrock’s Knowledge Base to generate high-quality vector embeddings of your documents, enabling efficient semantic search across your content.
• Intelligent Orchestration with LangChain or CrewAI: LangChain (or optionally CrewAI) is used to orchestrate the retrieval and augmentation process, seamlessly integrating the LLM’s reasoning with your private data for accurate and contextual responses.
• RAG API Deployment using FastAPI on Amazon ECS (Fargate): A FastAPI-based inference layer is deployed on ECS using AWS Fargate, providing a scalable and serverless containerized API endpoint to serve RAG responses.
• Secure Access with Application Load Balancer (ALB): The RAG API is exposed securely through an Application Load Balancer, with IP whitelisting implemented to ensure access control and compliance with security requirements.
• Infrastructure as Code (IaC) with AWS CDK (Python): All infrastructure is provisioned and managed using the AWS Cloud Development Kit (CDK) in Python, ensuring repeatable, version-controlled deployments across environments.
• CI/CD Automation with GitHub Actions: Deployment workflows are fully automated using GitHub Actions, enabling rapid, consistent delivery of application and infrastructure updates with built-in quality checks.

• Faster Time to Value: Start using RAG in production faster with a proven architecture and deployment pipeline.
• Enterprise Security: All components are deployed with AWS-native security best practices, including access controls and network isolation.
• Scalability and Maintainability: Fully containerized solution built with IaC and CI/CD pipelines ensures your system is ready to scale and evolve with your business needs.
• Tailored to Your Data: Incorporate your own documents, reports, and business content into the pipeline for contextualized LLM responses that reflect your domain knowledge.
• Future-Proof Architecture: Designed with modular components and modern tools to support future enhancements or integration with additional AI/ML workflows.

This offering is ideal for data science teams, enterprise architects, and technology leaders looking to harness the power of Generative AI while maintaining control over their data and infrastructure. Whether you're just starting out with RAG or need to productionalize an existing prototype, AllCode will deliver a robust, secure, and scalable solution tailored to your needs.