Placement, not lock-in
The technology architecture exists to run the layers above it anywhere the organization needs — managed cloud, hybrid, edge, on-premise, or sovereign — without rewriting applications. The guiding principle is placement: put compute and inference where data gravity, latency, and trust requirements demand, and keep the model of the system identical everywhere.
Model routing & inference
A reasoning system uses many models, not one. The runtime routes each request to an appropriate model based on policy: a hosted frontier model for open analytical work, a private model for sensitive material, or a local model where data cannot leave the premises. Routing is a policy decision, not a code change — so security posture and cost profile can shift without touching the application layer.
The technology stack
The runtime components that make placement and routing possible:
Model routing
Requests are routed to the right model — hosted, private, or local — by sensitivity, latency, and cost policy.
Compute & GPUs
Elastic accelerated compute for training and inference, from managed cloud GPUs to on-site clusters.
Storage
Graph, vector, and object storage for objects, embeddings, and evidence with tiered residency.
Networking
Private connectivity, service mesh, and controlled egress that hold across trust boundaries.
Local inference
Quantized and distilled models run at the edge or air-gapped, with no dependency on external endpoints.
Orchestration
Containerized services on Kubernetes give one deployable topology across every environment.
Portability across environments
Because the stack is containerized and the model of the system is defined declaratively, the same architecture deploys to a public cloud region, a customer data center, or an air-gapped enclave. This portability is what lets a single reference architecture serve a private-equity firm and a classified defense program alike — the subject of the deployment models and industry reference models.