R&D grounded in real constraints.
Our work focuses on building local-first autonomous systems that remain safe, understandable, and operationally useful — even when the environment is imperfect.
What we’re building
DAX is not a single feature — it’s a system. Our R&D is organized around building blocks that can be validated, measured, and integrated into dependable deployments.
Memory systems that stay fast
Designing local memory layers that preserve useful context without slowing inference. Goals include low-latency retrieval, clear summarization pipelines, and predictable storage growth.
Policy-driven autonomy
Building constrained workflows where actions are gated by policy, role, and approval. The objective is safe “autonomy” that is reviewable and reversible where appropriate.
Orchestration across nodes
Multi-node role separation (security, inference, storage, automation) with health signals and policy-aware workload placement.
Offline-first interfaces
Operator interfaces and mobile access designed for LAN-first reliability, intentional remote access, and clear visibility when the system is in degraded mode.
Auditing & accountability
Logging that is actually useful: who initiated an action, what changed, why it was allowed, and how to verify the outcome after the fact.
Model governance & updates
Safe model update strategies, rollback planning, and integrity verification so changes can be adopted without silently destabilizing behavior.
Measured progress
Our roadmap prioritizes verifiable outcomes: stability, security boundaries, and performance under constraints. We build systems that can be deployed, not just demonstrated.