Dynamic Adaptive eXosystem.
DAX is a privacy-first, offline-capable AI platform built to run on owner-controlled hardware. It is designed for resilience, governance, and long-term autonomy under defined boundaries.
Built like infrastructure.
DAX is designed to behave like a governed system: clear roles, clear permissions, and measurable performance. The goal is a platform that can be deployed, operated, and trusted over time.
Local-first intelligence
DAX prioritizes on-device and on-premise operation, limiting cloud dependence. When remote access is required, it should be explicit, authenticated, and logged.
Policy-aware automation
Automation is guided by authorization and audit requirements. DAX supports constrained execution pathways where sensitive actions require approval.
Operational visibility
Clear health reporting, service status, and events that can be reviewed after the fact. You should be able to understand what the system is doing and why.
Memory that stays useful
Local memory systems designed to retain relevant context without turning performance into a slow degradation curve. The objective is practical long-horizon usability.
Offline-capable workflow
DAX is designed to remain functional during connectivity loss. Critical operations should not depend on external services to behave safely.
Scalable architecture
Start on a single node, then grow into role-based clusters that separate security, inference, storage, and automation responsibilities.
Capabilities evolve through R&D.
DAX is developed through a research-and-validation cycle. Features are built, measured, hardened, and documented before they are treated as operationally reliable.