Measured computing
Hardware and models should be selected from real workload benchmarks, including response time, context limits, concurrency, power and thermals.
AXRYX / TECHNOLOGYPrivate AI, designed
Private AI, designed
from the hardware up.
A local-first technology approach joining computing hardware, AI models, governed agents and an operationally maintainable system.
A system, not a subscription
The AXRYX architecture.
Our design approach begins with the practical constraints: model sizes, latency, access controls, data residency, network isolation, power, maintainability and the use cases that justify deploying AI locally.
The aim is to give customers transparent control over what runs on the system and how it is maintained.
TECHNOLOGY STACK / CONCEPT
Apps & bounded AI agentsSUPERVISION
Local document search (RAG)TRACEABLE SOURCES
Model inference & orchestrationLOCAL RUNTIME
OS, permissions, audit & updatesADMINISTRATION
↓ HARDWARE INTEGRATION ↓
Compute / memory / storage / I/OPHYSICAL SYSTEM
Engineering principles
Four pillars of local intelligence.
Managed trust
Offline operation is not a security certification. Access, update provenance, logging and physical controls must be designed and verified for the deployment.
Traceable knowledge
Retrieval-augmented AI should reference approved source records and make it possible to check answers against underlying documentation.
Bounded autonomy
AI agents should operate within defined permissions, record actions and require human approval for consequential operations.
Deployment choices
Offline should mean what it says.
A deployment can be standalone, connected only to a trusted local network, or installed in a physically isolated environment. AXRYX is being developed with these models in mind. Core AI workloads should not require a cloud connection; updates and third-party integrations need their own explicit policy and verification.
Any claim of air-gap integrity, compliance with a specific security standard, or support for a particular AI model must be validated against the final system configuration.
Have a demanding use case?
We'd like to understand the model, data, security and connectivity constraints before recommending a system.