Public Release 9DA – structural governance and intent classification (github.com)

The recent public release of 9DA™ introduces a deterministic structural governance system designed specifically for classifying user intent based on input structure, without generating content or inferring meaning. Unlike conversational AI, 9DA focuses solely on determining the structural relationship between an input and the systems interacting with it, streamlining the processing of user inquiries into clear classifications such as "CONCEPTUAL," "EXTRACTION," and "AMBIGUOUS." This framework aims to enhance the governance capabilities of existing AI systems and create observable metrics around user interactions. Significantly, 9DA™ stands out for its rule-based approach, eliminating ambiguity in intent interpretation and providing measurable governance signals. By working as a governance layer alongside large language models or human-in-the-loop workflows, 9DA enables systems to assess boundary pressures and manipulation attempts without relying on probabilistic methods. This structured method not only enhances clarity in user intent classification but also promises a new way of observing and measuring AI governance dynamics, making it a noteworthy development for the AI/ML community focused on accountability and transparency in machine interactions.