Private inference
Run selected models locally where the workload and data policy call for it.
Private AI hardware
Dell GB10 for private, local AI.
Compact private AI does not remove the need for architecture and governance.
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.
Team experimentation
Give technical and business teams a governed environment for evaluation.
Hybrid architecture
Use local compute alongside approved cloud services rather than forcing one model for every workload.
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.