AWS Architecture

Learn about Qubitz AI's AWS-native architecture built on a unified data and AI layer for seamless GenAI workflow orchestration.

AWS-Native Architecture

Qubitz.ai's architecture is designed around a unified data and AI layer on AWS, enabling seamless orchestration of GenAI workflows. At its core, it connects data ingestion, model orchestration, and agentic execution in a modular, scalable, and secure manner.

Services

Core Services

Amazon Bedrock, Lambda, DynamoDB, and Amplify form the foundation of Qubitz AI's architecture.

Data Flow

Data Flow

Understanding how data flows through the system from ingestion to AI processing and response generation.

Security

Security & Governance

Enterprise-grade security, compliance, and governance features built into the architecture.

RAG

Vector Database & RAG

Amazon S3 combined with vector databases for scalable storage and retrieval of semantic embeddings.

Streaming

ECS & Fargate

Amazon ECS with AWS Fargate for managing containerized microservices and real-time streaming responses.

Architecture Overview

When a user interacts with the Qubitz platform through a web interface, their requests are routed via CloudFront and Amplify to the application layer, which includes authentication via Cognito and real-time APIs managed by AppSync and API Gateway.

The core logic for agent workflows is executed through AWS Lambda functions, which are responsible for invoking models, retrieving data, and managing tool usage dynamically.

On the backend, data flows into Amazon S3 or Aurora with pgvector for storage and semantic indexing. When retrieval-augmented generation (RAG) is used, relevant chunks are pulled from these sources using embedding services like Amazon Nova and connected directly to foundation models via AWS Bedrock and its knowledge base.

SageMaker components are integrated for model fine-tuning, asset management, and advanced ML workflows. This entire system is enhanced by optional lakehouse integration with Redshift and Glue for data analytics and governance.

┌─────────────────────────────────────────────────────────────────┐
│                        User Interface Layer                     │
├─────────────────────────────────────────────────────────────────┤
│  CloudFront  │  Amplify  │  Cognito  │  AppSync  │  API Gateway │
└─────────────────────────────────────────────────────────────────┘
                                │
                    ┌─────────────────────────────────┐
                    │        Application Layer        │
                    ├─────────────────────────────────┤
                    │  Lambda Functions (Orchestration)│
                    │  • Model Invocation             │
                    │  • Data Retrieval               │
                    │  • Tool Management              │
                    └─────────────────────────────────┘
                                │
                    ┌─────────────────────────────────┐
                    │         AI & Data Layer         │
                    ├─────────────────────────────────┤
                    │  Bedrock  │  S3 + Vector DB     │
                    │  Nova     │  Aurora + pgvector  │
                    │  SageMaker│  Redshift + Glue    │
                    └─────────────────────────────────┘
                                │
                    ┌─────────────────────────────────┐
                    │      Compute & Streaming        │
                    ├─────────────────────────────────┤
                    │  ECS + Fargate (Streaming)     │
                    │  • Real-time responses          │
                    │  • Containerized microservices │
                    └─────────────────────────────────┘

Core AWS Services Integration

Qubitz AI leverages services like Amazon Bedrock, AWS Lambda, DynamoDB, and Amplify for deployment and scaling. This tight AWS-native integration ensures low latency, streamlined governance policies, and seamless alignment with existing cloud infrastructure.

Amazon Bedrock Foundation

The platform uses Amazon Bedrock for access to foundational models such as Claude and Amazon Titan. Bedrock handles model management, including fine-tuning and Retrieval-Augmented Generation (RAG) workflows, without exposing the underlying infrastructure to users.

Key Benefits:

  • Model Variety - Access to Claude, Titan, Llama, and other leading models
  • Managed Service - No infrastructure management required
  • Fine-tuning Support - Custom model training capabilities
  • RAG Integration - Built-in knowledge base support

Serverless Orchestration via Lambda

Qubitz AI employs AWS Lambda for its backend logic processing AI prompts, orchestrating multi-agent solutions, and integrating with enterprise data. Serverless functions scale automatically and are cost-efficient under variable workloads.

Key Benefits:

  • Auto-scaling - Automatically scales with demand
  • Cost Efficiency - Pay only for compute time used
  • High Availability - Built-in fault tolerance and redundancy
  • Event-driven - Triggers based on user interactions and data changes

High-Performance Storage with DynamoDB

Workflow states, metadata, and session data are stored in Amazon DynamoDB, providing strong consistency and single-digit millisecond performance. This supports reliable and rapid data access throughout the AI lifecycle.

Key Benefits:

  • Low Latency - Single-digit millisecond response times
  • Scalability - Handles millions of requests per second
  • Consistency - Strong consistency guarantees
  • Durability - 99.999999999% durability

Front-End & Integration via Amplify

The no-code/visual builder, chat interfaces, and dashboards are delivered through AWS Amplify, which also incorporates authentication (Cognito) and direct connectivity to Bedrock and Lambda services as backend components.

Key Benefits:

  • Rapid Development - Pre-built UI components and authentication
  • Real-time Features - WebSocket support for live interactions
  • Global CDN - Content delivery via CloudFront
  • CI/CD Integration - Automated deployment pipelines

Data Flow Architecture

Request Processing Flow

  1. User Request - User interacts with web interface via CloudFront
  2. Authentication - Cognito validates user identity and permissions
  3. API Gateway - Routes request to appropriate Lambda function
  4. Lambda Processing - Orchestrates AI workflow and model invocation
  5. Data Retrieval - Fetches relevant data from S3, DynamoDB, or vector DB
  6. Model Invocation - Bedrock processes request with appropriate model
  7. Response Generation - ECS/Fargate streams response back to user

Data Storage Strategy

Structured Data (DynamoDB)

  • User sessions and conversation history
  • Workflow states and metadata
  • Configuration and settings
  • Performance metrics and analytics

Unstructured Data (S3)

  • Documents and knowledge base content
  • Media files and attachments
  • Logs and audit trails
  • Backup and archival data

Vector Data (Aurora + pgvector)

  • Semantic embeddings for RAG
  • Document chunks and metadata
  • Similarity search indexes
  • Knowledge base vectors

Security & Governance

Enterprise Security Features

  • IAM Integration - Role-based access control
  • VPC Support - Network isolation and security
  • Encryption - Data encryption at rest and in transit
  • Audit Logging - Comprehensive CloudTrail integration

Compliance & Governance

  • SOC 2 Compliance - Built-in compliance controls
  • GDPR Support - Data privacy and protection
  • HIPAA Ready - Healthcare compliance features
  • Financial Services - Regulatory compliance support

Scalability & Performance

Auto-scaling Capabilities

  • Lambda Scaling - Automatic function scaling based on demand
  • ECS Scaling - Container scaling for streaming workloads
  • Database Scaling - DynamoDB and Aurora auto-scaling
  • CDN Scaling - Global content delivery via CloudFront

Performance Optimization

  • Caching Strategy - Multi-layer caching for optimal performance
  • Load Balancing - Traffic distribution across multiple instances
  • Connection Pooling - Efficient database connection management
  • Response Streaming - Real-time response delivery

Integration Capabilities

AWS Service Integration

  • SageMaker - Model training and deployment
  • Redshift - Data warehousing and analytics
  • Glue - Data catalog and ETL processing
  • CloudWatch - Monitoring and alerting

Third-party Integrations

  • API Connectors - REST and GraphQL API support
  • Database Connectors - Support for various database types
  • File System Connectors - Integration with cloud storage
  • Messaging Systems - Event-driven architecture support

Getting Started with AWS Architecture

Ready to understand the technical foundation?

  1. Core Services - Deep dive into AWS services
  2. Data Flow - Understand data processing
  3. Security & Governance - Learn about security features
  4. Vector Database & RAG - Explore RAG capabilities
  5. ECS & Fargate - Understand streaming architecture

Qubitz AI's AWS-native architecture provides the foundation for enterprise-grade AI solutions with built-in scalability, security, and performance. The tight integration with AWS services ensures seamless operation and optimal resource utilization.