Development Lifecycle

Purpose

This document defines Rygen Technologies’ AI system development lifecycle, ensuring all AI systems are developed in accordance with ISO/IEC 42001:2023 requirements. It is intended to guide and inform the AI development process from end to end, ensuring that AI systems developed by Rygen are responsible and governed.

Scope

This lifecycle applies to all AI systems developed or substantially configured by Rygen:

• AI-powered features developed for X1 Platform (IPaaS)
• AI features developed for Corsair (TMS)
• Internal AI tools and automations developed by Rygen
• AI implementations requiring configuration, training, or customization

AI System Development Lifecycle

flowchart TD
    A[Problem Definition] --> B{Go / No Go?}
    B -->|No| C[Don't Implement]
    B -->|Yes| D[Data Collection and Preparation]
    D --> E[Training and Development]
    E --> F[Evaluation]
    F --> G{Acceptable?}
    G -->|No| D
    G -->|Yes| H[Deployment]
    H --> I[Monitoring]
    I --> J[Refinement]
    J --> D

Development Lifecycle Phases

Phase 1: Problem Definition

Purpose: Define the problem; assess impact and risk; evaluate feasibility; and document system design

Key Activities:

• Define the business problem and AI opportunity
• Conduct impact assessment
• Perform risk assessment
• Conduct feasibility study
• Make Go/No-Go decision based on assessments
• Establish System Design Specification

Deliverables:

• AI System Impact Assessment
• AI System Risk Report(s)
• AI System Feasibility Study
• Go/No-Go decision record
• AI System Design Specification

Definition of Done:

• Problem statement clearly documented
• Risks and impacts assessed and deemed acceptable
• Management approval obtained and recorded

Resources:

• Human Resources: Principal AI Engineer (impact assessment, risk assessment, feasibility evaluation); Product Manager or Technical Lead (requirements definition, business case); Relevant stakeholders (requirement gathering, approval)
• Tools: Jira (project tracking, decision records); Risk assessment templates; Impact assessment templates; GitLab (documentation repository)
• Data Resources: Historical system performance data (for feasibility assessment); Business/market requirements; Regulatory requirements (if applicable)
• Computing Resources: Access to documentation systems (GitLab, Confluence); Development environment (for technical feasibility)

Phase 2: Data Collection and Integration

Purpose: Prepare data and infrastructure for AI system development

Key Activities (system-dependent):

• For API Systems: Data flow documentation, integration testing
• For Custom Models: Full data collection and preparation per AI-011

Deliverables:

• For custom models: Data quality reports, prepared datasets
• For API systems: Integration specifications

Definition of Done:

• For custom models: All required data accessed and validated, data quality meets defined standards, processing steps documented
• For API systems: Integration points tested and operational, data flows documented

Resources:

• Human Resources: Data Engineers (data pipeline implementation); AI/ML Engineers (data requirements definition, quality assessment); DevOps Engineers (infrastructure setup for data storage/processing)
• Tools: Data pipeline tools (system-specific); Data quality assessment tools; Version control systems (GitLab); Data cataloging systems
• Data Resources: Source data systems and APIs; Training/validation/test datasets (for custom models); Data samples for integration testing (for API systems)
• Computing Resources: Data storage infrastructure; Data processing environments; Development databases/data lakes

Phase 3: Development

Purpose: Build and configure the AI system

Key Activities (system-dependent):

• For API-based systems: Configure API parameters, engineer prompts, build integration
• For custom models: Train models with prepared data, validate model performance
• Implement controls as required by risk assessment (explainability, human oversight, etc.)

Deliverables:

• For custom models: Trained models with performance metrics
• For API systems: Configured system with integration documentation
• Implementation of required controls per risk assessment

Definition of Done:

• System operational in development environment
• Required controls implemented and tested

Resources:

• Human Resources: AI/ML Engineers (model development for custom systems, API configuration); Software Engineers (integration development); DevOps Engineers (environment configuration)
• Tools: Development IDEs and frameworks; ML platforms (Vertex AI, etc.); Third-party AI APIs (OpenAI, Anthropic, Google, etc.); Version control (GitLab); CI/CD tools
• Data Resources: Prepared training datasets (custom models); API documentation and examples (API systems); Test data for validation
• Computing Resources: Development environments; Model training infrastructure (GPU/TPU for custom models); API service accounts and quotas

Phase 4: Evaluation

Purpose: Validate system readiness for deployment

Key Activities:

• Test against defined success criteria
• Validate performance metrics
• Conduct user acceptance testing (if applicable)
• Verify compliance requirements per risk assessment
• Document evaluation results

Deliverables:

• Evaluation report documenting test results and compliance verification
• User acceptance results (when conducted)

Definition of Done:

• Performance meets acceptable thresholds per system specifications
• Compliance requirements verified per risk assessment
• System approved for deployment
• Any critical issues resolved or accepted as residual risk

Resources:

• Human Resources: AI/ML Engineers (performance evaluation); QA Engineers (testing); Domain experts (validation); Principal AI Engineer (compliance verification)
• Tools: Evaluation frameworks and scripts; Testing tools; Performance monitoring tools; Compliance checklists
• Data Resources: Test datasets; Validation datasets; Benchmark data
• Computing Resources: Testing environments; Evaluation infrastructure

Phase 5: Deployment

Purpose: Deploy to production

Key Activities:

• Execute deployment plan
• Configure production monitoring
• Provide user guidance (documentation, training, or support as needed)

Deliverables:

• System deployed in production
• Monitoring and observability records

Definition of Done:

• System operational in production
• Monitoring and alerting active
• Users have access to necessary guidance for system use

Resources:

• Human Resources: DevOps Engineers (deployment execution); AI/ML Engineers (deployment validation); Technical Writers (documentation); Support Team (preparation for operational support)
• Tools: Deployment automation tools; Monitoring and observability platforms; Documentation systems; Communication platforms
• Data Resources: Production data access (controlled); Migration scripts and data
• Computing Resources: Production infrastructure; Monitoring systems; Backup systems

Phase 6: Monitoring

Purpose: Ensure ongoing performance

Key Activities:

• Monitor performance metrics
• Track and respond to incidents
• Collect user feedback
• Identify improvement needs
• Generate performance reports

Deliverables:

• Performance reports
• Incident reports (as needed)
• User feedback summary

Definition of Done:

• Continuous monitoring established
• Performance maintained within defined thresholds
• Incident response procedures operational
• Regular reporting cycle established

Resources:

• Human Resources: DevOps Engineers (infrastructure monitoring); AI/ML Engineers (performance monitoring); Support Team (incident response); Principal AI Engineer (compliance monitoring)
• Tools: GCP monitoring and logging; Custom monitoring dashboards; Alerting systems; Incident management tools (Jira)
• Data Resources: Production data streams; Performance metrics; User feedback data
• Computing Resources: Production systems; Monitoring infrastructure; Log storage systems

Phase 7: Refinement

Purpose: Continuously improve the system

Key Activities:

• Analyze monitoring data and feedback
• Implement improvements
• Update documentation
• Reassess risks as needed
• Plan next iteration

Deliverables:

• Improvement recommendations
• Updated system documentation
• Revised risk assessments (if needed)

Definition of Done:

• Improvement opportunities identified and prioritized
• Approved improvements implemented
• Documentation updated to reflect current state
• Decision made on next iteration timing

Resources:

• Human Resources: AI/ML Engineers (improvement implementation); Data Engineers (data pipeline updates); Product Managers (prioritization); Principal AI Engineer (risk reassessment)
• Tools: Development and testing tools (same as Phase 3); Analysis tools for feedback/metrics; Change management tools
• Data Resources: Performance data and metrics; User feedback; New training data (if applicable)
• Computing Resources: Development/staging environments; Analysis infrastructure

Resource Change Management

Changes to resources used by an AI system trigger updates to:

• AI System Design Specification (document resource changes)
• Risk assessments (if resource changes affect risk profile)
• Training requirements (if new tools or competencies needed)

Resource changes are tracked through:

• GitLab merge requests for documentation updates
• Jira tickets for implementation tracking
• Version control on all resource-related documentation

Decision Authority

• Low Risk Systems: Principal AI Engineer approval
• Medium Risk Systems: AI Governance Committee approval
• High Risk Systems: CTO approval required

Adaptive Implementation

For different system types:

• API-based systems: Phase 2 is minimal; Phase 3 focuses on configuration; resource needs are lighter (no model training infrastructure)
• Custom models: All phases fully implemented; requires full data pipeline and model training resources
• Internal tools: Simplified evaluation and deployment phases; may use existing organizational infrastructure

Resource requirements adapt based on system type and complexity. The AI System Design Specification documents actual resources used for each specific system.

Revision History