Legacy System AI Transformation

What we do?

Codot’s Legacy System AI Transformation service helps organisations breathe new life into ageing platforms by embedding AI capabilities. We assess your existing systems, identify high-impact AI opportunities—such as intelligent data extraction, predictive maintenance, process automation, and modern interfaces—and design a roadmap for integration. Our approach minimises disruption: we wrap or refactor legacy components, introduce AI-driven modules, and ensure seamless interoperability with modern services. The result is a future-ready system that leverages your existing investments while delivering improved efficiency, insights, and user experiences.

Outcomes You Can Expect

Enhanced Legacy Functionality: AI-driven features (e.g., intelligent data handling, predictive alerts, automated workflows) augment existing system capabilities.
Improved Efficiency: Automated processes and AI insights reduce manual effort, accelerate decision-making, and lower operational costs.
Seamless User Experience: Modern interfaces or AI-powered assistants integrated with legacy backends offer users a refreshed, intuitive interaction layer.
Data-Driven Insights: Consolidated data pipelines and AI analytics deliver actionable insights from legacy data stores without major migrations.
Reduced Risk & Disruption: Incremental, tested integration approach ensures system stability throughout transformation phases.
Future-Ready Platform: A hybrid architecture combining legacy strengths with modern AI and cloud components, positioned for ongoing innovation and scalability.

Why Choose Codot?

Experience with Legacy Platforms: Deep expertise in working with a variety of legacy technologies (monolithic apps, older databases, on-prem systems) and understanding their constraints.
Pragmatic AI Integration: We identify AI use-cases that deliver measurable value, then integrate via wrappers, microservices, or incremental refactoring to avoid risky rewrites.
Risk Mitigation: Phased transformation plans with proof-of-concept pilots, fallback mechanisms, and thorough testing to ensure stability throughout the process.
Seamless Interoperability: Design connectors and APIs that bridge legacy modules with AI services and modern cloud components without interrupting ongoing operations.
Security & Compliance: Maintain or enhance existing security postures, ensuring AI data flows and new interfaces meet regulatory and organisational standards.
Long-Term Partnership: Ongoing support, monitoring, and iterative enhancements keep transformed systems aligned with evolving business needs and technology advances.

Engagement Workflow

Assessment & Discovery: Analyse existing architecture, data sources, performance bottlenecks, and potential AI opportunities; define transformation goals and success metrics.
Pilot & Prototype: Develop small-scale AI proofs of concept (e.g., data extraction, anomaly detection) against legacy data or workflows to validate feasibility and ROI.
Architecture & Integration Planning: Design integration patterns—wrappers, microservices, API layers—to embed AI modules while preserving legacy stability.
Implementation & Testing: Build AI components (models, data pipelines, UI enhancements), integrate with legacy systems, and perform thorough functional, performance, and regression testing.
Deployment & Monitoring: Roll out AI-enhanced modules in a controlled manner, set up monitoring dashboards, logging, and alerts to track performance and user impact.
Iteration & Optimization: Collect feedback and metrics, refine models and integration layers, and plan further phases of transformation or new AI features under a flexible support agreement.