Hosted by default
Many hosted AI products place model execution on infrastructure controlled by a third party.
GIC technology
Private AI architecture, from models to infrastructure.
Learn how GIC approaches model execution, system boundaries, verification, and evidence-led engineering.
Why private AI infrastructure
AI ownership is an infrastructure problem, not just a model choice.
GIC is exploring what changes when the model, runtime, network boundary, and user experience are designed around operator control from the beginning.
Design objective: make private deployment, authentication, readiness, and evidence first-class system properties rather than afterthoughts.
Limited observability
Operators often see an API response, not the model runtime, readiness state, or execution environment behind it.
Operational dependency
Application availability, pricing, model access, and data handling can depend on an external provider.
Technology architecture
A private AI stack designed as one system.
GIC is developing the model, runtime, access, and verification layers together. The objective is straightforward: make private model operation easier to inspect, authenticate, and control.
Model and infrastructure developed together
Explicit readiness and authentication states
Evidence separated from architectural objectives
Research & development
Research is the evidence layer behind the products.
GIC publishes technical work so architecture claims can be traced back to experiments, source, measured results, and documented limitations.
Machine learning
Compact models, training experiments, interpretability, and constrained-compute research.
ExploreSystems
Runtimes, microkernels, protocols, and inspectable computing architecture.
ExploreVirtual Lab
Scientific simulation, instrumentation, evidence packaging, and reproducible workflows.
ExploreEvidence before claims
Research pages distinguish measured results, implementation details, design objectives, and open hypotheses rather than presenting them as equivalent evidence.