Private AI Runtime
Client-sovereign LLM infrastructure: no commands or data touch third-party cloud APIs./The AI runs in your building, on your machines.
Open-weight foundation models run on customer-controlled GPU servers, behind identity, policy gates and an immutable evidence log./You choose where the AI lives. It stays on hardware you own and nobody else can read your data.
What goes wrong without it
Commercial cloud APIs receive sensitive prompts and commands./Your private questions go to someone else's servers.
Sizing is guessed rather than benchmarked./Hardware is bought on a hunch.
Models are treated as authoritative sources of state./The AI is trusted to know facts it should not decide.
Four steps, in order
Size
Benchmark real workloads before purchase (Gate 2)./Test on your own work first.
Isolate
Pin drivers and isolate the runtime on vLLM or SGLang./Run it in a sealed box.
Ground
Tenant-isolated retrieval with source citation./Answers use your documents only.
Govern
Gateway wraps every call./The checkpoint protects the model too.
Key capabilities
Pilot
1× RTX PRO 6000 Blackwell 96 GB or 1× H100 80 GB with Qwen3.8-27B (Q4)./One server to prove it works.
Enterprise
16× H200 or 16× B200 with Kimi K3 (2.8T MoE)./A cluster for heavy use.
Context
262k native, extendable to 1M tokens./Reads very long documents.
Indicative sizing
Sizing is confirmed by your benchmarks, not promised./We measure before you buy.
Before and after
A ministry's prompts leave the country through a vendor API./Sensitive questions leave your country.
The same workload runs on in-country GPUs with no external model calls./The same work happens on local machines.
Illustrative scenarios. Controls shown are design intent, confirmed by the gates on your own infrastructure.
Related
See it on your own agents.
Architecture by Salem Salah bin Omair AlShamsi. Delivered with our technology partner.