1. AI Deployment & MLOps
Turn AI models into real, working systems
- Production deployment using Docker & Kubernetes
- End-to-end MLOps pipelines (build → deploy → monitor)
- Observability, reliability, and performance tuning
2. Platform Architecture & Modernisation
Build or upgrade systems that scale with confidence
- Distributed, Linux-native architecture design
- Legacy system modernisation (containerisation, cloud readiness)
- Scalable, secure, and cost-efficient infrastructure
3. System Integration & Automation
Make complex systems operate as one
- API and data pipeline integration
- Workflow automation and orchestration
- Self-healing systems to reduce operational overhead
4. Technical Advisory & Delivery Governance
Keep decisions sharp and execution disciplined
- Architecture review and risk assessment
- AI strategy aligned to business outcomes
- Execution oversight from concept to production