Deploy sovereign AI you can audit.
Enterprise-grade on-prem/hybrid LLM systems: compliance-by-design (GDPR, NIS2, AI Act), end-to-end observability, and predictable TCO.
<20ms/token
Predictable TCO
Audit-ready
EU-based • NDA-first • Reply within 24h
Start here (60 seconds)
Pick your situation and get a guided next step. We will recommend a tool, a resource, and the right engagement package.
Recommendation
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You will see the best next page and the engagement package that fits your context.
Results at a glance
Chat p95 latency
18 ms
Throughput
500 tok/s
TCO savings
72%
Availability
99.9%
Infrastructure
K8s vs Bare-metal
Inference
Triton/ORT vs vLLM
Vector Store
FAISS vs Milvus
GitOps
ArgoCD/Flux
Decisions & Trade-offs
Every sovereign AI deployment requires critical architectural decisions. Here are the key trade-offs we navigate:
Cloud vs On-Premise
Trade convenience for control. On-prem means full data sovereignty but requires infrastructure investment.
Model Size vs Latency
Larger models offer better accuracy but impact response time. Right-sizing is critical for production SLAs.
Batch vs Streaming
Batch processing maximizes throughput; streaming minimizes time-to-first-token. Choose based on user experience requirements.
GPU Utilization vs Cost
Higher GPU utilization reduces TCO but may increase queue times during peak load.
Key KPIs
Production-grade AI infrastructure requires monitoring these critical performance indicators:
Latency
p50: <10 ms/token
p95: <20 ms/token
p99: <35 ms/token
Throughput
Requests/sec: 200+
Tokens/sec: 500+
Concurrent: 50+
Reliability
Uptime: 99.9%
MTTR: <5 min
MTBF: >720 hrs
Efficiency
GPU Util: 80%+
Memory Util: 75%+
TCO vs Cloud: -72%
Real-World Deployments
Production sovereign AI implementations across industries.
Finance
Knowledge assistant for compliance-heavy banking operations
Engagements (enterprise-grade)
Fixed-scope, outcome-driven engagements. Start with an Assessment, then ship a Pilot, then harden to Production.
Assessment
2 weeks
Best for: teams that need a fast, compliant roadmap and architecture decisions.
- Scope & constraints workshop (security, data residency, regulatory scope)
- Compliance map (GDPR / NIS2 / AI Act) + risk register
- Target architecture + SLOs + rollout plan
- TCO model + “build vs buy” trade-offs
- Executive-ready roadmap (owners, milestones, evidence)
Pilot
4-6 weeks
Best for: shipping a scoped use case with measurable quality, safety, and performance.
- Working pilot for a core workflow (support, NOC, research, sales enablement)
- Private/hybrid inference stack (vLLM/Triton) + RAG pipeline
- Security controls (SSO, ACLs, auditing) + observability (metrics/tracing)
- Eval harness (quality, safety) + go/no-go criteria
- Docs + training + production plan
Production
8-12 weeks / retainer
Best for: hardening, scaling, and operating with SLOs, audits, and predictable economics.
- Production deployment with SLOs, runbooks, and incident response
- Continuous evaluation + feedback loop + model lifecycle governance
- Security hardening (secrets/KMS, network segmentation, SIEM integration)
- Cost optimization: GPU utilization, caching, batching, and capacity planning
- Handover to your team or ongoing retainer
Let’s scope your deployment.
Tell me your use case, data sensitivity, and timeline. I will reply with a concrete next step (assessment, pilot, or production hardening).
To move fast, include:
- Use case + stakeholders (who uses it, and why)
- Data & constraints (GDPR/NIS2/AI Act, EU residency, air-gap)
- Timeline + success KPIs (latency, quality, cost)