AI Principles
The principles that guide how Dazvix evaluates, builds and deploys AI-enabled systems for clients, from privacy to accountability.
- Updated
- October 6, 2026
Human control
AI should amplify human capability, not remove accountability. Important decisions need human ownership, clear escalation paths and the ability to override or stop automated behavior.
Usefulness over novelty
We recommend AI when it solves a real workflow problem, improves quality or reduces meaningful friction. We do not add AI to products just because it is fashionable.
Privacy by design
AI systems should minimise data collection, avoid unnecessary retention, protect sensitive information and make data flows understandable to the people responsible for them.
Verifiable quality
AI features need evaluation, test cases, monitoring and failure handling. For factual, financial, legal, medical or safety-sensitive use cases, the burden of proof is higher.
Clear user experience
People should understand when they are interacting with AI, what the system can and cannot do, and how to reach a human or correct the output when needed.