aws_bedrock_kendra_architecture - fleXRPL/contractAI GitHub Wiki

AWS Bedrock + Kendra Architecture for Contract AI Memory System

Leveraging AWS-native services for enterprise-grade RAG implementation

Overview

Amazon Bedrock combined with Amazon Kendra provides an ideal foundation for Contract AI's persistent memory architecture. This AWS-native approach delivers enterprise-grade security, scalability, and integration capabilities while dramatically reducing implementation complexity and time-to-market.

By leveraging existing AWS enterprise connectors and managed services, Contract AI can focus on core AI agent logic and business outcomes rather than building foundational RAG infrastructure from scratch.

Architecture Benefits

Enterprise Integration Advantages

Pre-Built Connectors:

  • Document Systems: SharePoint, Confluence, Google Workspace, Box, Dropbox
  • Ticketing Systems: ServiceNow, JIRA, Zendesk, Freshservice
  • Communication Platforms: Slack, Microsoft Teams, Workplace
  • Development Tools: GitHub, GitLab, Bitbucket, Azure DevOps
  • Database Systems: RDS, DynamoDB, Redshift, S3 data lakes

Security and Compliance:

  • Data Encryption: At-rest and in-transit encryption by default
  • Access Control: Integration with AWS IAM and customer identity systems
  • Compliance Frameworks: SOC 2, HIPAA, PCI DSS, FedRAMP certifications
  • VPC Integration: Private network connectivity for sensitive enterprise data

Scalability and Reliability:

  • Auto-Scaling: Handles enterprise workloads without manual intervention
  • High Availability: Built-in redundancy and disaster recovery
  • Global Deployment: Multi-region support for international enterprises
  • Performance SLAs: Enterprise-grade uptime and response time guarantees

Cost and Operational Benefits

Reduced Development Overhead:

  • No Infrastructure Management: Fully managed services eliminate operational complexity
  • Faster Time-to-Market: Pre-built connectors accelerate integration timeline
  • Lower Technical Risk: Proven enterprise infrastructure reduces implementation risk
  • Simplified Compliance: Inherit AWS certifications and security controls

Predictable Scaling Costs:

  • Pay-per-Use Model: Costs scale directly with customer usage
  • No Upfront Investment: Eliminate capital expenditure for infrastructure
  • Transparent Pricing: Clear cost structure for customer billing
  • Volume Discounts: Enterprise pricing tiers for large-scale deployments

Technical Architecture

Core Components

1. Amazon Kendra as Knowledge Repository

Enterprise Data Sources → Kendra Connectors → Intelligent Index → Query Interface

Knowledge Ingestion:

  • Continuous Sync: Real-time updates from connected enterprise systems
  • Intelligent Parsing: Automatic extraction of metadata, relationships, and context
  • Content Understanding: Natural language processing for semantic indexing
  • Version Control: Tracking of document changes and historical versions

Query Processing:

  • Semantic Search: Understanding user intent beyond keyword matching
  • Contextual Ranking: Relevance scoring based on user role and current task
  • Multi-Source Aggregation: Combining results from multiple enterprise systems
  • Access Control Enforcement: Respecting source system permissions and security

2. Amazon Bedrock for AI Processing

Kendra Results → Context Assembly → Bedrock Models → Agent Responses → Action Execution

Model Flexibility:

  • Claude 3.5 Sonnet: Advanced reasoning and analysis capabilities
  • Llama 2/3: Cost-effective processing for routine operations
  • Amazon Titan: AWS-native models optimized for enterprise use
  • Model Switching: Dynamic selection based on task complexity and cost optimization

Enterprise Features:

  • Custom Model Fine-Tuning: Organization-specific model training
  • Guardrails: Content filtering and safety controls
  • Audit Logging: Complete tracking of model interactions and decisions
  • Data Residency: Regional data processing for compliance requirements

3. Contract AI Agent Framework

Bedrock Responses → Agent Logic → Enterprise System APIs → Operational Actions

Agent Capabilities:

  • Context Preservation: Maintain conversation and operational context across sessions
  • Multi-System Operations: Coordinate actions across multiple enterprise platforms
  • Approval Workflows: Integration with existing approval and change management processes
  • Error Handling: Graceful failure management with automatic escalation

Data Flow Architecture

Real-Time Knowledge Ingestion

graph TD
    A[Enterprise Systems] --> B[Kendra Connectors]
    B --> C[Data Processing Pipeline]
    C --> D[Kendra Index]
    D --> E[Real-Time Updates]
    E --> F[Contract AI Agents]

Data Sources Integration:

  1. Operational Systems: JIRA tickets, ServiceNow incidents, monitoring alerts
  2. Documentation: Confluence pages, GitHub wikis, procedure documents
  3. Communication: Slack channels, email threads, meeting transcripts
  4. Configuration: Infrastructure as Code, deployment configurations, policy documents
  5. Historical Data: Past incidents, solutions, performance metrics, audit logs

Query and Response Flow

graph TD
    A[User Query/System Event] --> B[Context Analysis]
    B --> C[Kendra Query]
    C --> D[Relevant Knowledge Retrieval]
    D --> E[Context Assembly]
    E --> F[Bedrock Model Processing]
    F --> G[Agent Decision Logic]
    G --> H[Action Execution]
    H --> I[Result Logging]
    I --> J[Knowledge Update]

Implementation Strategy

Phase 1: Foundation Setup (Months 1-2)

AWS Infrastructure Deployment:

  • Bedrock model access configuration and testing
  • Kendra instance setup with initial enterprise connectors
  • IAM roles and security policies for enterprise integration
  • VPC configuration for secure enterprise connectivity

Initial Data Sources:

  • Priority 1: Documentation systems (Confluence, GitHub wikis)
  • Priority 2: Ticketing systems (JIRA, ServiceNow)
  • Priority 3: Communication platforms (Slack, Teams)

Basic Agent Development:

  • Simple query-response functionality using Kendra + Bedrock
  • Basic context preservation across conversation turns
  • Integration with primary enterprise authentication systems

Phase 2: Advanced Integration (Months 3-4)

Extended Connectivity:

  • Custom connectors for monitoring systems (CloudWatch, Datadog, New Relic)
  • Integration with deployment tools (Jenkins, GitLab CI, AWS CodePipeline)
  • Connection to infrastructure management (Terraform, CloudFormation)

Enhanced Agent Capabilities:

  • Multi-step reasoning and planning across multiple systems
  • Proactive monitoring and alerting based on historical patterns
  • Automated response to common operational scenarios

Security Hardening:

  • Enterprise SSO integration (SAML, OIDC)
  • Fine-grained access controls based on user roles and data sensitivity
  • Comprehensive audit logging and compliance reporting

Phase 3: Production Optimization (Months 5-6)

Performance Tuning:

  • Query optimization for faster response times
  • Model selection optimization for cost and accuracy
  • Caching strategies for frequently accessed knowledge

Advanced Features:

  • Predictive analytics based on historical operational data
  • Automated runbook execution for standard operational procedures
  • Integration with change management and approval workflows

Scalability Preparation:

  • Multi-tenant architecture for customer isolation
  • Auto-scaling configuration for variable workloads
  • Disaster recovery and business continuity planning

Enterprise Integration Patterns

Secure Connectivity Options

VPC Peering:

Customer VPC ↔ Contract AI VPC ↔ AWS Services VPC
  • Direct private network connectivity
  • No internet traffic for sensitive data
  • Customer maintains full network control

PrivateLink Integration:

Customer On-Premises → Direct Connect → VPC Endpoints → AWS Services
  • Secure connectivity for hybrid environments
  • Compliance with strict data residency requirements
  • Integration with existing enterprise network infrastructure

API Gateway Pattern:

Customer Systems → API Gateway → Lambda Functions → Bedrock/Kendra
  • RESTful API interface for customer integration
  • Rate limiting and authentication controls
  • Simplified integration for customer development teams

Data Governance and Compliance

Data Classification:

  • Public: General documentation and procedures
  • Internal: Operational data and configuration information
  • Confidential: Security policies and sensitive system information
  • Restricted: Personal data and regulated information

Access Control Matrix:

Role Public Internal Confidential Restricted
Operations Team Read/Write Read/Write Read No Access
Security Team Read Read/Write Read/Write Read
Management Read Read Read No Access
Contract AI Agent Read Read/Write Read* No Access

*With approval workflow for modifications

Audit Requirements:

  • Complete logging of all data access and modifications
  • Real-time alerting for unauthorized access attempts
  • Quarterly access reviews and permission validation
  • Annual compliance assessments and certifications

Cost Optimization Strategies

Tiered Service Levels

Basic Tier ($10K-25K/month):

  • Kendra: Single index with basic connectors
  • Bedrock: Llama 2 models for cost optimization
  • Storage: Standard retrieval with 24-hour indexing
  • Support: Business hour availability

Professional Tier ($25K-75K/month):

  • Kendra: Multiple indexes with advanced connectors
  • Bedrock: Claude 3.5 Sonnet for complex reasoning
  • Storage: Intelligent tiering with real-time indexing
  • Support: 24/7 availability with dedicated account management

Enterprise Tier ($75K-200K/month):

  • Kendra: Custom indexes with specialized connectors
  • Bedrock: Multi-model optimization with custom fine-tuning
  • Storage: High-performance with advanced analytics
  • Support: Dedicated infrastructure and technical account team

Usage-Based Pricing Model

Kendra Costs:

  • Index maintenance: $1,000/month per index
  • Query volume: $0.003 per query
  • Storage: $0.30 per GB per month
  • Connector usage: $0.50 per connected system per month

Bedrock Costs:

  • Claude 3.5 Sonnet: $0.003 per 1K input tokens, $0.015 per 1K output tokens
  • Llama 2 70B: $0.00065 per 1K input tokens, $0.00265 per 1K output tokens
  • Custom model fine-tuning: $30 per hour of training time

Total Cost Example (Mid-Size Enterprise):

  • Kendra: $5,000/month (5 indexes, 50K queries, 500GB storage)
  • Bedrock: $8,000/month (1M tokens/day average)
  • Infrastructure: $2,000/month (compute, networking, storage)
  • Total AWS Costs: $15,000/month
  • Contract AI Service Fee: $35,000/month
  • Customer Savings vs. Traditional: $50,000/month (60% reduction)

Risk Mitigation and Contingency Planning

Technical Risks

AWS Service Outages:

  • Mitigation: Multi-region deployment with automatic failover
  • Contingency: Offline mode with cached knowledge and basic operations
  • SLA Protection: Credit mechanisms for customer impact compensation

Model Performance Degradation:

  • Mitigation: Multi-model architecture with automatic switching
  • Contingency: Human escalation workflows for complex scenarios
  • Monitoring: Real-time performance tracking with automated alerts

Data Integration Failures:

  • Mitigation: Redundant connector architecture with health monitoring
  • Contingency: Manual data sync procedures and alert escalation
  • Recovery: Automated retry mechanisms with exponential backoff

Business Risks

AWS Pricing Changes:

  • Mitigation: Long-term enterprise agreements with price protection
  • Contingency: Multi-cloud architecture preparation for migration
  • Customer Protection: Fixed-price contracts with internal cost absorption

Compliance Violations:

  • Mitigation: Automated compliance monitoring and validation
  • Contingency: Immediate data isolation and investigation procedures
  • Legal Protection: Comprehensive insurance and legal framework

Competitive Response:

  • Mitigation: Focus on customer outcomes rather than technology features
  • Contingency: Platform differentiation through specialized industry knowledge
  • Innovation: Continuous enhancement of agent capabilities and integrations

Success Metrics and KPIs

Technical Performance Metrics

System Reliability:

  • Uptime Target: 99.9% availability (8.7 hours downtime/year)
  • Response Time: <3 seconds for standard queries, <10 seconds for complex analysis
  • Accuracy Rate: >95% correct responses based on available knowledge
  • Knowledge Freshness: <15 minutes lag for critical operational data

Integration Health:

  • Connector Uptime: 99.5% availability for each enterprise system connection
  • Data Sync Success: >99% successful synchronization attempts
  • Error Recovery: <5 minutes to restore failed connections
  • Coverage Completeness: >90% of relevant enterprise data indexed

Business Impact Metrics

Operational Efficiency:

  • Incident Response Time: 75% reduction in time to resolution
  • Problem Recurrence: 80% reduction in repeat issues
  • Knowledge Accessibility: 90% reduction in time to find relevant information
  • Automation Rate: 60% of routine tasks handled without human intervention

Customer Success:

  • Cost Reduction: Average 45% decrease in operational expenses
  • Reliability Improvement: 95% reduction in unplanned downtime
  • Team Productivity: 40% increase in strategic work vs. operational tasks
  • Customer Satisfaction: Net Promoter Score >70

Financial Performance

Revenue Metrics:

  • Annual Recurring Revenue: Target $50M by Year 3
  • Customer Acquisition Cost: <$25K per enterprise customer
  • Customer Lifetime Value: >$500K average contract value
  • Net Revenue Retention: >130% through expansion and optimization

Cost Management:

  • AWS Infrastructure Costs: <30% of customer subscription fees
  • Gross Margin: >70% after all direct costs
  • Unit Economics: Positive contribution margin within 6 months
  • Scalability Factor: 10x customer growth with 3x infrastructure cost increase

Conclusion

Amazon Bedrock and Kendra provide the ideal foundation for Contract AI's enterprise memory architecture, offering:

  • Accelerated Development: Pre-built enterprise connectors and managed infrastructure
  • Enterprise Credibility: AWS security, compliance, and reliability standards
  • Scalable Economics: Pay-per-use model that scales with customer growth
  • Risk Mitigation: Proven platform reduces technical and business risks

This AWS-native approach enables Contract AI to focus on core differentiators - persistent AI agents and guaranteed business outcomes - while leveraging world-class infrastructure for enterprise integration and scalability.

The combination of Kendra's enterprise knowledge capabilities and Bedrock's advanced AI models creates a powerful foundation for delivering the "Technology Success Platform" vision with enterprise-grade reliability and security.

Leveraging AWS infrastructure to democratize enterprise technology expertise