This article outlines the principles and processes our team follows when developing or deploying AI systems and crypto solutions.

Core principles

We prioritise transparency, reproducibility and clear documentation across research, development and deployment phases to reduce ambiguity for stakeholders.

Risk management includes threat modelling, controlled testing and staged rollouts to limit unintended effects while preserving iterative improvement cycles.

Operational process

Our operational workflow emphasises multidisciplinary review, reproducible experiments and measurable acceptance criteria at every stage of delivery.

• Ideation and requirements gathering involve cross-functional input from product, engineering and legal teams to define measurable objectives and constraints.
• Model development follows documented experiments with version control, reproducible datasets and explicit performance metrics for continuous validation.
• Pre-deployment reviews assess privacy, security and economic harms using third-party audits or red-teaming where appropriate and documented.
• Post-deployment monitoring combines automated telemetry and human oversight to detect regressions and enable rapid remediation when issues emerge.

Governance and compliance

We align with applicable regulations and industry standards, maintaining audit trails and clear accountability for decision points in systems lifecycle.

Stakeholder communication relies on concise reports, impact summaries and accessible explanations to support informed decision-making by organisational leaders and partners.

Regular reviews update policies and operational practices in response to field experience, audit findings and evolving legal expectations.