AI that belongs inside a system, not outside it.
We treat machine learning as one component of a larger engineering architecture. A useful AI system requires data, evaluation, deployment, monitoring, security, user experience, governance, and clear human responsibility.
Areas
- Computer vision
- Medical imaging AI
- Speech recognition
- Natural language processing
- Multimodal AI
- Agentic systems
- Retrieval-augmented generation
- Edge AI
- Model optimisation
- Evaluation systems
Model lifecycle
- 1.Problem definition
- 2.Data strategy
- 3.Model selection
- 4.Experimentation
- 5.Evaluation
- 6.Safety review
- 7.Integration
- 8.Deployment
- 9.Monitoring
- 10.Continuous improvement