AI systems are powerful, but they are not always predictable. As organizations deploy AI more widely, they encounter familiar challenges: hallucinated outputs, behavioral drift, and inconsistent responses across contexts.
At the same time, governments and industries around the world are beginning to establish expectations for how AI systems should behave in real-world environments. Together, these forces are creating a growing need for better infrastructure around how AI is deployed and governed.
ArchI, developed by Principle 20, takes a different approach.
Rather than attempting to change the AI model itself, ArchI is a model-agnostic constraint architecture that addresses two operational challenges: keeping AI out of prohibited domains, and keeping AI generation within approved sources. It works alongside existing AI technologies, allowing organizations to continue using the models they choose while adding structure, oversight, and reliability to AI deployments.