We start with a co-creation concept, collaboratively  identifying the business goals & challenges, with a thorough analysis of the existing systems and requirements, and an assessment of the data and AI maturity levels within the organisation. This phase prioritises data and AI-driven initiatives and launches pilot projects that align with strategic business objectives  and defines a strategic roadmap for implementation.

The design phase focuses on designing a robust and scalable solution tailored to the organisation's needs. It ensures the infrastructure is ready to support the new system and lays out an implementation plan that includes expected deliverables, the projected impact, and criteria for success. This phase is crucial for setting up a strong foundation before developing.

During development and deployment, there's a deep understanding and pre-processing of data to ensure the model is prepped for optimal performance, ensure full-scale implementation for developed models. Evaluating the model's performance and results to ensure it meets the desired objectives. Seamlessly integrate developed models into the existing system while maintaining data accuracy and integrity.

The delivery phase focused on ensuring a robust quality assurance process to maintain the standards expected by stakeholders. Validating data accuracy and integrity, guarantee the reliability, security, and effectiveness of these systems to provide value continuously, alongside comprehensive enablement programs to support the end-users.

24/7 management of models & apps for optimal performance on needed infrastructure. Continuous monitoring & optimisation for peak system function. Effectiveness is regularly assessed against business goals to ensure alignment with the organisation's evolving needs and value delivery.