Sovereign LLM architecture, production-grade knowledge engines, agentic systems
engineered to reason and decide. For CIOs and CDOs who want production AI, not
PowerPoint POCs.
What we see in the field
Most enterprise AI projects stall at the POC stage. Models perform in demos but
collapse in production because there is no data architecture, no monitoring and no
improvement loop. The result: significant investment, minimal delivered value and
growing skepticism from the business. The gap is not in model capability — it
is in engineering discipline.
3 services
Sovereign AI Architecture
Design of sovereign LLM architectures hosted in Europe. Selection of open-weight models (Mistral, Llama, Qwen), GPU sizing, data security, GDPR and NIS2 compliance. Integration with internal data platforms (Snowflake, Databricks) and enterprise directories. No vendor lock-in, no data leakage.
Multi-agent systems with planning, task decomposition, external tool integration and guardrails. Framework selection (LangGraph, CrewAI, custom) calibrated to criticality. Robustness testing, loop management, decision logging and action reversibility. Production-ready, not demo-ready.
Reference architecture: 7 layers
Every production AI system we build follows this layered architecture. Each layer
is independently testable, observable and replaceable.
01
Data Governance
Classification, lineage, access control
02
Ingestion & ETL
Document parsing, chunking, versioning
03
Embedding & Indexing
Vector store, hybrid search, metadata
04
Orchestration
Re-ranking, fallback, routing, caching
05
LLM Inference
Model selection, prompt management, guardrails
06
Agent Layer
Planning, tool use, memory, reversibility
07
Observability
Evaluation, drift detection, feedback loops
Frequently asked questions
How do you deploy a RAG system in production?
A production RAG requires five building blocks: a versioned ingestion pipeline, a performant vector store, robust orchestration (re-ranking, fallback, caching), automatic response evaluation and a continuous improvement loop. The prompt is the least important component. Without these layers, you have a demo, not a system.
Azure or AWS for enterprise AI?
Azure offers the deepest native integration with OpenAI and the Microsoft ecosystem (M365, Fabric). AWS via Bedrock provides access to Anthropic, Mistral and Cohere with superior flexibility. For European sovereignty, OVH and Scaleway are maturing rapidly with LLM-as-a-Service offerings on SecNumCloud-qualified infrastructure.
What does a sovereign AI deployment cost?
Infrastructure runs 5K to 50K EUR/month depending on GPU requirements and model size. Implementation typically takes 3 to 6 months for a focused RAG use case, 6 to 12 months for a multi-agent platform. The real cost driver is data preparation, not model inference. Budgets that skip data engineering overshoot by 3x.
How do you select the right LLM in 2026?
Three axes: task fit (reasoning depth vs. speed), sovereignty requirement (open-weight vs. API), and total cost of ownership (inference cost at scale, fine-tuning investment, maintenance burden). We benchmark candidates on your actual data before recommending. Model rankings from public leaderboards are unreliable for enterprise workloads.
How do you guarantee data security in AI deployments?
Three levers: hosting in a controlled European zone (OVH SecNumCloud, Outscale), open-weight models executed on-premises or in a private cloud, and explicit contractual terms on data retention and training exclusion. Public OpenAI and Anthropic APIs do not meet sovereign criteria for regulated industries.
What is agentic reasoning and when does it apply?
Agentic reasoning means the AI system can plan, decompose tasks, call external tools and self-correct. Mature use cases: structured document generation, knowledge base exploitation, IT workflow automation, regulatory research. Fully autonomous agents on critical business decisions remain premature. The key is calibrating autonomy to the risk profile of each task.
