On-Premise, Cloud, or Hybrid — Sovereign by Design

AI-Ready Data & Enterprise AI

Build a sovereign, GDPR-compliant AI infrastructure that fits your architecture — on your hardware, in the cloud, or both. Full auditability, zero compromises.

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Key Metrics

68%

Avg. data quality improvement

4×

Reduction in AI project failure rate

120 hrs/yr

GDPR audit preparation time saved

< 18 months

Typical payback period

The Challenge

Most enterprises sit on years of valuable data locked in incompatible silos — ERP exports, CRM dumps, unstructured documents. Without a clean, governed foundation, AI projects fail at the data layer before a single model is trained.

Our Solution

We architect a data lakehouse that unifies all sources under a single semantic layer — deployed on your own hardware, in a private cloud, or as a hybrid combination. Your AI models train on your data, under your legal jurisdiction, with complete GDPR auditability built in.

Technical Implementation

Data profiling and quality assessment across all source systems
Flexible lakehouse deployment: on-premise (MinIO / Apache Iceberg / DuckDB) or private cloud
Semantic data catalog with lineage tracking
Local or cloud LLM deployment (LLaMA / Mistral / Azure OpenAI) with RAG pipelines
Role-based access control and full audit logging
Continuous data quality monitoring dashboards

Frequently Asked Questions

How do you ensure GDPR compliance whether we use on-premise or cloud AI?

On-premise deployments keep all personal data inside your own data centre. For cloud deployments we use EU-based private cloud environments (no data leaves your legal jurisdiction) with the same field-level encryption, role-based access control, and immutable audit logs — all auditable by your DPO. The right architecture depends on your data classification and risk profile; we advise on the best fit.

What is a data lakehouse and why does it matter for AI?

A lakehouse combines the low-cost storage of a data lake with the ACID transaction guarantees of a data warehouse. For AI workloads, this means you can train models on the full historical dataset while maintaining consistent, query-ready tables for analytics.

Can we run large language models on-premise, in the cloud, or both?

Yes to all three. On-premise, we deploy open-weight models such as LLaMA 3 and Mistral on your GPU servers via Ollama or vLLM. For cloud workloads we integrate Azure OpenAI or AWS Bedrock inside your private tenant. Hybrid setups are common — sensitive data stays local while general-purpose inference scales in the cloud.