Departmental AI
Support larger models, more users and shared organizational workloads.
Private AI hardware
Departmental and rack-scale private AI infrastructure.
When private AI becomes a shared organizational capability, infrastructure has to be designed as a system.
Toronto-based Canadian teamGovernance built into deliveryHuman accountability
Workload fit
Start with workload, data sensitivity and operating requirements.
AFA does not treat hardware as a generic box sale. The design should reflect model size, concurrency, memory, storage, networking, security, data location, user workflows and support.
Integrated infrastructure
Coordinate compute, storage, networking, security and facility requirements.
AI factory readiness
Design a path from pilot systems toward scalable private AI infrastructure.
Architecture
Private and sovereign AI design
Decide what should run locally, what may use cloud services and how information, models and users are governed.
Explore sovereign AI →Procurement
Turn requirements into an infrastructure plan
Translate workloads into a practical compute, storage, networking and implementation sequence.
Procurement readiness →Bring us the decision, workload or capability you need to move forward.
We can help determine the right strategy, governance, training, architecture or infrastructure starting point.