AI can help modernize - even your legacy environments

AI Modernization is the process of upgrading old (legacy) IT systems, applications, and infrastructure by integrating modern AI and cloud technologies to improve efficiency, automate tasks, enhance security, and enable new capabilities, often by augmenting rather than completely replacing existing systems. It involves using AI to analyze legacy code, automate complex migrations, transform business logic, and build intelligent, agile, and future-proof IT environments.

Automated code analysis

Scanning millions of lines of old code to identify inefficiencies, vulnerabilities, and dependencies, and then automatically translating it into modern programming languages.

Data Migration

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Automate, accelerate, and secure the complex process of moving data and applications to the cloud, leveraging AI for intelligent analysis, data cleansing, risk prediction, and post-migration optimization, significantly reducing manual effort, errors, and downtime.

Data Integration

Involves centralizing diverse data, applying AI for automated insights (like predictive maintenance or personalization), and using integration tools (ETL/ELT) to pipeline data for real-time analytics, creating a foundation for intelligent applications and automated decision-making across the business.

Key steps include:

Centralizing storage with IBM, AWS or Azure for examples
Customizing data ingestion using ETL tools such as AWS Glue
Implementing meta data management and governance - AI can help in this laborious process!

Testing and documentation automation

Automate repetitive tasks, enhance efficiency, and improve coverage/accuracy, thereby freeing up your people to focus on more strategic and creative work.

Areas to consider:

Test Case Generation
Test Automation and Execution
Self-Healing Tests
Defect Analysis and Prediction
Performance Testing
Test Data Creation / Generation

AI SaaS Tool Implementation

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Implementing a SaaS AI tool involves defining clear goals, assessing data, building a Minimum Viable Product (MVP) with core AI features, integrating it into a scalable platform, ensuring security, and then launching, monitoring, and continuously optimizing for user feedback and business outcomes.

Key steps include:

Strategy and Planning: Identifying user pain points, define goals and use cases to deploy iteratively and prioritize, tech stack selection, Security Compliance and Legal approvals.
Development and Integration: Build your MVP, data preparation, model training, and ongoing iteration for improvement and scaling.
Launch and Post Go-Live: Testing and QA approvals, Deployment, ongoing Monitoring. Hand off to Product Owner.

AI Opportunity Assessment

An AI Assessment is a strategic process to identify, evaluate, and prioritize where Artificial Intelligence can add significant value to a business, helping move from idea to action by pinpointing high-impact use cases, analyzing potential ROI, assessing readiness, and creating a practical roadmap for implementation, ensuring alignment with business goals and ethical considerations. It answers questions like "Where can AI help?" and "How do we start?"

Can include:

Opportunity Identification
Impact Analysis
Readiness Assessment
Prioritization
Roadmap Creation