
# AI Integration for Small and Medium Businesses

**AI Integration** is the process of incorporating artificial intelligence technologies into existing business processes and systems to improve efficiency, decision-making, and customer experience. For small and medium businesses (SMBs), AI integration represents a significant opportunity to compete with larger organizations while optimizing limited resources.

## Definition and Scope

AI integration encompasses the strategic implementation of artificial intelligence technologies across various business functions. Unlike enterprise AI deployments, SMB AI integration focuses on practical, cost-effective solutions that provide immediate value while building a foundation for future growth.

### Key Components of AI Integration

1. **Data Infrastructure**: Establishing the foundation for AI systems
2. **Process Mapping**: Identifying automation opportunities
3. **Technology Selection**: Choosing appropriate AI solutions
4. **Implementation Strategy**: Phased deployment approach
5. **Change Management**: Ensuring organizational adoption

## Common AI Integration Use Cases for SMBs

### Customer Service and Support

- **AI Chatbots**: Handle common customer inquiries 24/7
- **Email Classification**: Automatically route and prioritize support tickets
- **Sentiment Analysis**: Monitor customer satisfaction in real-time
- **Knowledge Base Integration**: Provide instant access to product information

### Marketing and Sales

- **Lead Scoring**: Automatically qualify and prioritize prospects
- **Content Personalization**: Tailor marketing messages to individual customers
- **Predictive Analytics**: Forecast sales trends and customer behavior
- **Social Media Monitoring**: Track brand mentions and engagement

### Operations and Administration

- **Document Processing**: Automate invoice processing and data entry
- **Appointment Scheduling**: Intelligent calendar management
- **Inventory Management**: Predictive stock level optimization
- **Quality Control**: Automated inspection and defect detection

### Financial Management

- **Expense Categorization**: Automatic transaction classification
- **Fraud Detection**: Identify suspicious financial activities
- **Cash Flow Prediction**: Forecast financial needs and opportunities
- **Budget Optimization**: AI-driven resource allocation

## Implementation Strategy

### Phase 1: Assessment and Planning (Weeks 1-4)

#### Business Process Analysis

1. **Identify Pain Points**: Document time-consuming, repetitive tasks
2. **Map Current Workflows**: Create detailed process diagrams
3. **Calculate ROI Potential**: Estimate time and cost savings
4. **Prioritize Opportunities**: Rank by impact and implementation difficulty

#### Technology Assessment

1. **Evaluate Current Systems**: Audit existing software and infrastructure
2. **Data Quality Review**: Assess data availability and quality
3. **Integration Requirements**: Identify compatibility needs
4. **Budget Planning**: Allocate resources for AI implementation

### Phase 2: Foundation Building (Weeks 5-12)

#### Data Infrastructure Setup

1. **Data Collection**: Implement systems to gather relevant data
2. **Data Cleaning**: Establish processes for data quality maintenance
3. **Storage Solutions**: Set up secure, scalable data storage
4. **Access Controls**: Implement appropriate security measures

#### Technology Selection

1. **Off-the-Shelf Solutions**: Evaluate existing AI platforms
2. **Custom Development**: Assess need for tailored solutions
3. **Integration Capabilities**: Ensure compatibility with existing systems
4. **Scalability Planning**: Choose solutions that grow with your business

### Phase 3: Pilot Implementation (Weeks 13-20)

#### Start Small

1. **Choose One Process**: Begin with a single, well-defined use case
2. **Set Clear Metrics**: Define success criteria and measurement methods
3. **Limited Scope**: Implement in a controlled environment
4. **Gather Feedback**: Collect input from users and stakeholders

#### Testing and Validation

1. **Performance Testing**: Ensure AI systems meet accuracy requirements
2. **User Acceptance**: Validate that solutions meet business needs
3. **Integration Testing**: Verify compatibility with existing systems
4. **Security Review**: Confirm data protection and privacy compliance

### Phase 4: Scale and Optimize (Weeks 21+)

#### Gradual Expansion

1. **Roll Out Successfully**: Expand to additional processes
2. **Monitor Performance**: Track key metrics and outcomes
3. **Continuous Improvement**: Refine and optimize AI systems
4. **Training and Support**: Ensure team adoption and proficiency

## Technology Considerations

### Cloud vs. On-Premises

- **Cloud Solutions**: Lower upfront costs, easier maintenance
- **On-Premises**: Greater control, potentially lower long-term costs
- **Hybrid Approach**: Combine both for optimal flexibility

### AI Platform Options

- **No-Code Platforms**: Quick implementation, limited customization
- **Low-Code Solutions**: Balance of ease and flexibility
- **Custom Development**: Maximum control, higher complexity
- **API Integration**: Connect existing systems with AI capabilities

### Data Requirements

- **Minimum Data Sets**: Establish baseline requirements for AI training
- **Data Quality**: Ensure accuracy, completeness, and consistency
- **Privacy Compliance**: Adhere to relevant data protection regulations
- **Security Measures**: Protect sensitive business and customer data

## Common Challenges and Solutions

### Challenge 1: Limited Technical Expertise

**Solution**: Partner with AI consultants or use no-code/low-code platforms that don't require deep technical knowledge.

### Challenge 2: Budget Constraints

**Solution**: Start with high-ROI, low-cost implementations and gradually expand as benefits are realized.

### Challenge 3: Data Quality Issues

**Solution**: Invest in data cleaning and management processes before AI implementation.

### Challenge 4: Change Resistance

**Solution**: Provide comprehensive training and demonstrate clear benefits to encourage adoption.

### Challenge 5: Integration Complexity

**Solution**: Choose AI solutions with strong integration capabilities or work with experienced implementation partners.

## ROI Measurement and KPIs

### Key Performance Indicators

- **Time Savings**: Hours saved per process per month
- **Cost Reduction**: Direct cost savings from automation
- **Accuracy Improvement**: Reduction in errors and rework
- **Customer Satisfaction**: Improved response times and service quality
- **Employee Productivity**: Increased output per employee

### ROI Calculation Methods

1. **Direct Cost Savings**: Calculate reduced labor and operational costs
2. **Efficiency Gains**: Measure time savings and productivity improvements
3. **Revenue Impact**: Track increased sales and customer retention
4. **Quality Improvements**: Quantify error reduction and quality enhancements

## Best Practices for SMB AI Integration

### 1. Start with Clear Objectives

- Define specific business problems to solve
- Set measurable success criteria
- Align AI initiatives with business goals

### 2. Focus on High-Impact, Low-Complexity Solutions

- Prioritize quick wins that demonstrate value
- Choose implementations with clear ROI
- Build momentum with early successes

### 3. Ensure Data Quality and Security

- Establish robust data management practices
- Implement appropriate security measures
- Comply with relevant regulations

### 4. Plan for Scalability

- Choose solutions that can grow with your business
- Consider future integration needs
- Build flexible, adaptable systems

### 5. Invest in Change Management

- Provide comprehensive training
- Communicate benefits clearly
- Address concerns and resistance proactively

## Security and Compliance Considerations

### Data Protection

- **Encryption**: Ensure data is encrypted in transit and at rest
- **Access Controls**: Implement role-based access permissions
- **Audit Logging**: Track system access and data usage
- **Backup and Recovery**: Establish robust data protection procedures

### Regulatory Compliance

- **GDPR**: Ensure compliance with data protection regulations
- **Industry-Specific**: Adhere to relevant industry standards
- **Privacy Policies**: Update policies to reflect AI usage
- **Consent Management**: Obtain appropriate user consent

## Future Trends in SMB AI Integration

### Emerging Technologies

- **Edge AI**: Processing AI closer to data sources
- **Federated Learning**: Collaborative AI without sharing raw data
- **Explainable AI**: Transparent decision-making processes
- **AI Ethics**: Responsible AI development and deployment

### Market Evolution

- **Industry-Specific Solutions**: Tailored AI for specific business types
- **Simplified Implementation**: Easier deployment and management
- **Cost Reduction**: More affordable AI solutions for SMBs
- **Integration Standards**: Improved compatibility between systems

## Related Terms

- [Artificial Intelligence](/knowledge-base/ai-machine-learning/artificial-intelligence)
- [Machine Learning](/knowledge-base/ai-machine-learning/artificial-intelligence)
- [Digital Transformation](/knowledge-base/business-strategy/digital-transformation)
- [DevOps](/knowledge-base/cloud-infrastructure/devops)

## Implementation Checklist

### Pre-Implementation

- [ ] Conduct business process analysis
- [ ] Identify high-impact automation opportunities
- [ ] Assess current data quality and infrastructure
- [ ] Define clear success metrics and KPIs
- [ ] Secure budget and stakeholder approval

### Implementation

- [ ] Choose appropriate AI solutions and platforms
- [ ] Set up data infrastructure and security measures
- [ ] Implement pilot program with limited scope
- [ ] Train team members on new systems
- [ ] Monitor performance and gather feedback

### Post-Implementation

- [ ] Measure ROI and performance improvements
- [ ] Optimize and refine AI systems
- [ ] Expand to additional processes
- [ ] Document lessons learned and best practices
- [ ] Plan for future AI initiatives

---

_AI integration represents a significant opportunity for SMBs to level the playing field with larger competitors. By following a structured approach and focusing on practical, high-impact implementations, small and medium businesses can successfully leverage AI to improve efficiency, reduce costs, and enhance customer experience._


Comprehensive guide to integrating AI solutions into small and medium business operations, including implementation strategies and best practices

AI integration refers to the process of incorporating artificial intelligence technologies into existing business processes and systems

Small and medium businesses (SMBs) typically have 1-500 employees and annual revenue under $50 million

AI Adoption in Small Business: A Comprehensive Guide

Practical AI Implementation for SMBs

AI Integration for Small and Medium Businesses


# DevOps

**DevOps** is a set of practices that combines software development (Dev) and IT operations (Ops) to shorten the development lifecycle while delivering features, fixes, and updates frequently in close alignment with business objectives.

## Definition

DevOps is both a cultural philosophy and a set of practices that emphasizes collaboration, communication, and integration between software developers and IT operations professionals. It aims to automate and streamline the process of software delivery and infrastructure changes.

## Core Principles

### 1. Culture and Collaboration

- **Shared Responsibility**: Developers and operations teams share responsibility for the entire software lifecycle
- **Cross-functional Teams**: Teams work together across traditional boundaries
- **Continuous Learning**: Emphasis on experimentation and learning from failures
- **Transparency**: Open communication and visibility across all processes

### 2. Automation

- **Infrastructure as Code**: Managing and provisioning infrastructure through code
- **Continuous Integration/Continuous Deployment (CI/CD)**: Automated testing and deployment pipelines
- **Configuration Management**: Automated configuration and environment management
- **Monitoring and Logging**: Automated monitoring and alerting systems

### 3. Measurement and Feedback

- **Metrics-driven**: Using data to drive decisions and improvements
- **Continuous Monitoring**: Real-time visibility into application and infrastructure performance
- **User Feedback**: Rapid feedback loops with end users
- **Performance Metrics**: Tracking deployment frequency, lead time, and failure rates

## DevOps Practices

### Continuous Integration (CI)

- Developers frequently merge code changes into a central repository
- Automated builds and tests run on every code change
- Early detection of integration problems
- Maintains a consistently deployable codebase

### Continuous Deployment (CD)

- Automated deployment of code changes to production
- Reduced manual intervention and human error
- Faster time to market
- Improved reliability through smaller, more frequent deployments

### Infrastructure as Code (IaC)

- Managing infrastructure through configuration files
- Version control for infrastructure changes
- Consistent and repeatable deployments
- Reduced configuration drift

### Monitoring and Observability

- Real-time monitoring of applications and infrastructure
- Centralized logging and tracing
- Proactive alerting and incident response
- Performance optimization based on data

## Benefits

### For Development Teams

- **Faster Delivery**: Reduced time from code commit to production deployment
- **Improved Quality**: Automated testing catches issues early
- **Better Collaboration**: Shared tools and processes across teams
- **Reduced Risk**: Smaller, more frequent changes are easier to troubleshoot

### For Operations Teams

- **Increased Stability**: Automated processes reduce human error
- **Better Visibility**: Comprehensive monitoring and logging
- **Faster Recovery**: Automated rollback and recovery procedures
- **Improved Efficiency**: Reduced manual tasks and repetitive work

### For Business

- **Competitive Advantage**: Faster time to market
- **Cost Reduction**: Reduced manual effort and faster problem resolution
- **Improved Customer Satisfaction**: More frequent updates and faster bug fixes
- **Better Resource Utilization**: More efficient use of development and operations resources

## Common Tools and Technologies

### Version Control

- Git, GitHub, GitLab, Bitbucket

### CI/CD Platforms

- Jenkins, GitLab CI, GitHub Actions, CircleCI, Travis CI

### Configuration Management

- Ansible, Chef, Puppet, SaltStack

### Infrastructure as Code

- Terraform, CloudFormation, Pulumi

### Containerization

- Docker, Kubernetes, Docker Compose

### Monitoring and Logging

- Prometheus, Grafana, ELK Stack, Splunk

## Implementation Strategy

### Phase 1: Foundation

1. **Assess Current State**: Evaluate existing processes and identify pain points
2. **Build Cross-functional Teams**: Create teams with both development and operations expertise
3. **Implement Version Control**: Ensure all code and configuration is version controlled
4. **Establish Basic CI**: Set up automated builds and tests

### Phase 2: Automation

1. **Automate Testing**: Implement comprehensive automated testing
2. **Implement CD**: Automate deployment processes
3. **Infrastructure as Code**: Convert manual infrastructure to code
4. **Monitoring Setup**: Implement comprehensive monitoring and alerting

### Phase 3: Optimization

1. **Performance Optimization**: Use metrics to identify and fix bottlenecks
2. **Security Integration**: Integrate security into the CI/CD pipeline
3. **Advanced Monitoring**: Implement advanced observability and tracing
4. **Continuous Learning**: Establish feedback loops and improvement processes

## Common Challenges

### Cultural Resistance

- **Solution**: Start with small wins and demonstrate value
- **Approach**: Focus on collaboration and shared goals
- **Timeline**: Cultural change takes time and consistent effort

### Tool Complexity

- **Solution**: Start simple and add complexity gradually
- **Approach**: Choose tools that fit your team's skills and needs
- **Timeline**: Implement tools incrementally

### Legacy Systems

- **Solution**: Modernize incrementally where possible
- **Approach**: Use containers and microservices to modernize gradually
- **Timeline**: Plan for long-term modernization

## Relevance to Our Services

At Opertus Systems, we help organizations:

- **DevOps Assessment**: Evaluate current practices and identify improvement opportunities
- **CI/CD Implementation**: Set up automated build, test, and deployment pipelines
- **Infrastructure Automation**: Convert manual processes to code-based automation
- **Monitoring and Observability**: Implement comprehensive monitoring solutions
- **Team Training**: Provide training and coaching for DevOps practices
- **Tool Selection**: Help choose and implement appropriate DevOps tools

## Related Terms

- [Continuous Integration](/knowledge-base/software-development/continuous-integration)
- [Agile Development](/knowledge-base/software-development/agile-development)

## Further Reading

For organizations implementing DevOps, we recommend:

1. **Start Small**: Begin with one team or project as a pilot
2. **Focus on Culture**: Emphasize collaboration and shared responsibility
3. **Automate Gradually**: Build automation incrementally
4. **Measure Everything**: Establish metrics and track progress
5. **Learn Continuously**: Encourage experimentation and learning from failures

---

_DevOps represents a fundamental shift in how organizations develop and deliver software. By breaking down silos and emphasizing collaboration, automation, and continuous improvement, DevOps enables organizations to deliver value faster and more reliably._


Definition and explanation of DevOps methodology, practices, and its role in modern software development

DevOps combines 'Development' and 'Operations' to emphasize collaboration between these traditionally separate teams

The term DevOps was first coined by Patrick Debois in 2009 during the Agile 2008 conference

The DevOps Handbook

Accelerate: The Science of Lean Software and DevOps

DevOps


# Continuous Integration (CI)

**Continuous Integration (CI)** is a software development practice where developers frequently merge their code changes into a central repository, after which automated builds and tests are run. The goal is to provide rapid feedback so that if a defect is introduced into the code base, it can be identified and corrected as soon as possible.

## Definition

Continuous Integration is a development practice that requires developers to integrate code into a shared repository several times a day. Each check-in is then verified by an automated build, allowing teams to detect problems early. By integrating regularly, you can detect errors quickly, and locate them more easily.

## Core Principles

### 1. Frequent Integration

- **Daily Commits**: Developers commit code at least once per day
- **Small Changes**: Prefer small, incremental changes over large batches
- **Shared Repository**: All developers work from the same codebase
- **Mainline Development**: Work directly on the main branch or trunk

### 2. Automated Build Process

- **Automated Compilation**: Code is automatically compiled on every commit
- **Automated Testing**: All tests run automatically after each build
- **Fast Feedback**: Build and test process completes quickly
- **Reliable Builds**: Build process is consistent and repeatable

### 3. Self-Testing Build

- **Comprehensive Testing**: Unit tests, integration tests, and acceptance tests
- **Test Coverage**: High percentage of code covered by tests
- **Automated Deployment**: Successful builds can be automatically deployed
- **Quality Gates**: Build fails if tests fail or quality thresholds aren't met

## Benefits for Software Development

### 1. Early Bug Detection

- **Immediate Feedback**: Problems are caught within minutes of being introduced
- **Reduced Debugging**: Issues are easier to locate and fix
- **Lower Costs**: Fixing bugs early is much cheaper than fixing them later
- **Improved Quality**: Continuous testing leads to higher code quality

### 2. Faster Development

- **Reduced Integration Problems**: Frequent integration prevents merge conflicts
- **Confidence in Changes**: Developers can make changes with confidence
- **Faster Releases**: Automated processes speed up the release cycle
- **Parallel Development**: Multiple developers can work simultaneously

### 3. Better Collaboration

- **Shared Responsibility**: All team members are responsible for build health
- **Transparent Process**: Everyone can see the current state of the codebase
- **Knowledge Sharing**: Code reviews and pair programming are encouraged
- **Team Accountability**: Build failures affect the entire team

## Implementation Strategies

### 1. Setting Up CI

#### Choose a CI/CD Platform
- **Jenkins**: Open-source automation server
- **GitHub Actions**: Integrated with GitHub repositories
- **GitLab CI**: Built into GitLab platform
- **Azure DevOps**: Microsoft's CI/CD platform
- **CircleCI**: Cloud-based CI/CD service

#### Configure Build Pipeline
- **Source Code Management**: Connect to your version control system
- **Build Triggers**: Set up automatic builds on code changes
- **Build Steps**: Define compilation, testing, and deployment steps
- **Notifications**: Configure alerts for build status

### 2. Build Pipeline Components

#### Source Stage
- **Code Checkout**: Retrieve latest code from repository
- **Branch Management**: Handle different branches appropriately
- **Dependency Resolution**: Install required dependencies

#### Build Stage
- **Compilation**: Compile source code
- **Code Analysis**: Run static analysis tools
- **Artifact Creation**: Generate deployable artifacts

#### Test Stage
- **Unit Tests**: Run automated unit tests
- **Integration Tests**: Test component interactions
- **Acceptance Tests**: Verify business requirements
- **Performance Tests**: Check performance characteristics

#### Deploy Stage
- **Staging Deployment**: Deploy to test environment
- **Production Deployment**: Deploy to production (if automated)
- **Rollback Capability**: Ability to revert changes quickly

### 3. Best Practices

#### Build Management
- **Keep Builds Fast**: Aim for builds under 10 minutes
- **Parallel Execution**: Run independent tasks in parallel
- **Caching**: Cache dependencies and build artifacts
- **Incremental Builds**: Only rebuild what has changed

#### Testing Strategy
- **Test Pyramid**: More unit tests, fewer integration tests, even fewer UI tests
- **Test Isolation**: Tests should not depend on each other
- **Test Data Management**: Use consistent test data
- **Mock External Dependencies**: Mock external services in tests

## Tools and Technologies

### CI/CD Platforms

#### Cloud-Based Solutions
- **GitHub Actions**: Integrated with GitHub, supports many languages
- **GitLab CI**: Built into GitLab, powerful pipeline features
- **CircleCI**: Fast builds, good for open source projects
- **Travis CI**: Popular for open source, supports many languages
- **Azure DevOps**: Microsoft ecosystem integration

#### Self-Hosted Solutions
- **Jenkins**: Most popular open-source CI server
- **GitLab**: Self-hosted GitLab with CI/CD
- **TeamCity**: JetBrains' CI/CD platform
- **Bamboo**: Atlassian's CI/CD solution

### Build Tools

#### Java
- **Maven**: Popular build and dependency management tool
- **Gradle**: Flexible build system with Groovy DSL
- **Ant**: Traditional build tool for Java

#### JavaScript/Node.js
- **npm**: Package manager and build tool
- **Yarn**: Alternative to npm with better performance
- **Webpack**: Module bundler for web applications

#### .NET
- **MSBuild**: Microsoft's build engine
- **dotnet CLI**: Cross-platform .NET tools
- **NuGet**: Package manager for .NET

#### Python
- **pip**: Python package installer
- **setuptools**: Package distribution utilities
- **tox**: Testing automation tool

## Measuring CI Success

### Key Metrics

- **Build Success Rate**: Percentage of successful builds
- **Build Duration**: Time to complete builds
- **Test Coverage**: Percentage of code covered by tests
- **Deployment Frequency**: How often code is deployed

### Quality Indicators

- **Code Quality**: Static analysis results
- **Technical Debt**: Accumulated technical debt
- **Bug Rate**: Number of bugs introduced
- **Time to Recovery**: Time to fix broken builds

## Advanced CI Techniques

### 1. Feature Branch Workflows

- **Git Flow**: Structured branching model
- **GitHub Flow**: Simplified branching for continuous deployment
- **Trunk-Based Development**: Working directly on main branch
- **Branch Protection**: Prevent direct commits to main branch

### 2. Advanced Testing

- **Parallel Testing**: Run tests in parallel to speed up execution
- **Test Sharding**: Split tests across multiple machines
- **Flaky Test Management**: Handle unreliable tests
- **Test Data Management**: Manage test data effectively

### 3. Security Integration

- **Static Application Security Testing (SAST)**: Analyze source code for vulnerabilities
- **Dynamic Application Security Testing (DAST)**: Test running applications
- **Dependency Scanning**: Check for known vulnerabilities in dependencies
- **Container Scanning**: Scan container images for vulnerabilities

## Challenges and Solutions

### 1. Common Challenges

- **Build Time**: Long build times can slow down development
- **Flaky Tests**: Unreliable tests that fail intermittently
- **Environment Differences**: Differences between development and CI environments
- **Complex Dependencies**: Managing dependencies across environments

### 2. Solutions

- **Build Optimization**: Parallel execution, caching, and incremental builds
- **Test Reliability**: Better test isolation and data management
- **Environment Consistency**: Use containers and infrastructure as code
- **Dependency Management**: Use lock files and version pinning

### 3. Team Adoption

- **Training**: Provide training on CI/CD practices
- **Documentation**: Clear documentation of processes
- **Feedback Loops**: Regular review of CI/CD effectiveness
- **Continuous Improvement**: Regular assessment and improvement

## Future Trends

### 1. AI and Machine Learning

- **Intelligent Test Selection**: AI-driven test prioritization
- **Predictive Analytics**: Predict build failures before they happen
- **Automated Code Review**: AI-assisted code review
- **Smart Notifications**: Intelligent alerting based on context

### 2. Cloud-Native CI/CD

- **Serverless CI/CD**: Pay-per-use CI/CD services
- **Kubernetes Integration**: Native Kubernetes CI/CD
- **Multi-Cloud Support**: CI/CD across multiple cloud providers
- **Edge Computing**: CI/CD for edge deployments

### 3. Security-First CI/CD

- **Shift Left Security**: Security testing earlier in the pipeline
- **Compliance Automation**: Automated compliance checking
- **Zero Trust CI/CD**: Security-first CI/CD practices
- **Supply Chain Security**: Secure software supply chains

## Conclusion

Continuous Integration is a fundamental practice in modern software development that provides immediate feedback on code quality and helps teams deliver software more reliably. By automating the build and test process, CI reduces the risk of integration problems and enables faster, more confident development.

The key to successful CI implementation is starting with the basics and gradually adding more sophisticated features. Focus on getting fast, reliable builds first, then add comprehensive testing, and finally implement advanced features like automated deployment.

---

*This article provides a comprehensive overview of Continuous Integration. For specific implementation guidance or training, [contact our team](mailto:inquiries@opertus.systems) to discuss how we can help your organization implement CI/CD practices.* 

Comprehensive explanation of Continuous Integration practices, tools, and benefits for modern software development

Continuous Integration was first introduced by Grady Booch in 1991 and later popularized by Martin Fowler in 2000

CI/CD stands for Continuous Integration/Continuous Delivery, representing the complete automation pipeline

Tag: Automation

AI Integration for Small and Medium Businesses

DevOps

Continuous Integration (CI)