Enhancing human-AI interaction through mathematical precision.
We research and develop infrastructure that enables AI systems to better understand human intent. Our approach orients models with structure and clarity to create truly human-first AI.
Purpose Framework
Purpose is a mathematical framework for AI behavioral coherence. It's not driven by any one AI system, but it's capable of grounding all of them. It prevents semantic drift and distortion through mathematical constraints to make human-AI interactions safer and more predictable.
Purpose augments clarity in AI-mediated systems and end-user interfaces, turning intent into renewable momentum: information, feedback, collaboration, action.
Core Mission
To create better ways to diversify where and how people invest their digital capital (attention), make online interactions more meaningful, and foster real-world flourishing by preserving intent across human-machine systems.
Core Shifts:
- Shaping a more meaningful internet
- Helping people reclaim attention
- Shifting vanity to value creation
- Bridging self and knowledge
- Advancing AI alignment
- Enhancing AI safety
Why Now
Agentic models are scaling faster than semantic infrastructure. Without mathematical grounding, these systems will default to efficiency over ethics. That future is being built now, and Purpose aims to restore meaning in interactions by preventing drift between human intent and AI systems, enabling interoperability and alignment.
Mathematical Foundation
Our frameworks provide validated mathematical foundations for AI behavioral coherence. Cross-model testing demonstrates consistent parameter relationships that produce predictable, auditable behavior across different AI systems without requiring model retraining.
These mathematical principles suggest possibilities for advanced applications: cross-model continuity that preserves context across different AI systems, intent maps that maintain semantic coherence over extended interactions, and ephemeral interfaces that emerge based on clarity requirements.
While these applications remain theoretical, they follow logically from the proven mathematical relationships.
Validated capabilities include:
- Consistent behavioral outcomes across multiple frontier models
- Mathematical constraints that preserve human agency
- Real-time semantic drift detection and correction
- Auditable decision paths for safety verification
What becomes achievable? AI systems that maintain coherent understanding of human intent across domains, while providing transparent reasoning for their decisions.
Long-Term Vision
We're building infrastructure for the next generation of human-AI interaction.
We're thinking beyond apps and chatbots. We believe semantic infrastructure is next. Today's digital landscape forces humans to become interface managers, juggling dozens of specialized applications to accomplish basic tasks. This fragmentation creates cognitive overhead that undermines the very productivity these tools promise to deliver.
Purpose envisions a fundamental shift from app-based interactions to semantic infrastructure that understands human intent holistically. Instead of context-switching between artificial boundaries, people will interact with unified intelligence that maintains coherence across domains.
Consider Tony Stark's relationship with Jarvis, one system that understands context across every domain rather than requiring separate applications for each function. We don't believe this is science fiction. We think it's a welcomed engineering challenge with proven mathematical foundations.
When semantic infrastructure handles computational complexity transparently, technology becomes genuinely helpful rather than extractive, enabling focus on human flourishing rather than attention maximization.
For details on the math and probes that validate this foundation, choose a path below.
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