Defining How AI Operates Inside Regulated Systems
INTEKNIQUE develops governance-first frameworks, operating models, and reference implementations for regulatory-viable AI in life sciences and other GxP environments.
The rules that make AI inspectable, accountable, and acceptable.
The Problem
AI adoption is outpacing governance. Regulated organizations are deploying models, agents, and automated workflows faster than they can explain, validate, or defend them under inspection.
Existing guidance focuses heavily on what to document. INTEKNIQUE focuses on how AI-enabled systems must behave to remain acceptable over time.
What INTEKNIQUE Does
INTEKNIQUE develops governance frameworks that define how AI-enabled systems can operate responsibly inside regulated environments.
- How AI systems manage context, assumptions, and constraints
- How evidence is generated continuously, not retroactively
- How accountability is preserved when humans are not in every loop
- How validation evolves as systems change
This work bridges regulation, system architecture, and operational reality.
What This Is Not
INTEKNIQUE does not sell AI tools. We do not compete with platform vendors. We do not chase feature velocity.
Our focus is the layer above tools: the governance that determines whether AI can be trusted in regulated environments at all.
How the Work Is Expressed
Frameworks become meaningful only when tested against real-world constraints. INTEKNIQUE evaluates governance ideas through internal reference systems—non-commercial implementations used to explore how regulatory expectations translate into system behavior.
These reference systems exist to validate principles, not to create product dependency. They serve as evidence that governance-first design can function in practice.