Hyperscaler AI ships your data out
Hyperscaler AI assumes data leaves the perimeter to be processed. Your supervisor does not. DORA, the EU AI Act, and national regulators draw a line that contracts alone cannot cross.
INDEV Sovereign AI
We deploy, fine-tune, and operate domain-specific LLMs inside European banks — air-gapped, audited, and under SLA.
Production AI, operated inside your perimeter.
INDEV Sovereign AI deploys, fine-tunes, and operates domain-specific LLMs inside the regulatory perimeter of European banks. Your data stays in your environment, every inference is audited, and the model is trained on your corpus and operated under SLA. The same operational discipline that has kept INDEV's regulated software running in systemic banks for twenty years — now applied to the AI layer.
Why existing options fail
Open-source AI has no operations
Open-source LLM tooling is developer-grade. No SLA. No audit trail. No lifecycle management. No incident response. Running it in production requires an operating team most banks don't have.
Retrieval alone doesn't scale
Retrieval-only architectures don't scale to enterprise corpora. Decades of regulated documents don't fit in a context window — and don't want to be re-tokenized on every query.
You don't control the model lifecycle
Hosted-inference contracts tie your application to a vendor's model versioning, deprecation, and pricing. When their model changes, yours follows. DORA treats this as third-party concentration risk.
What we operate
Inference serving
vLLM-based serving with an OpenAI-compatible API, deployed on your GPU pool inside the cluster. No external endpoints. No internet egress.
Audit and observability
Self-hosted Langfuse captures every prompt, response, and trace. Full audit trail stays inside your perimeter.
Evaluation
Per-field eval harnesses measure model and quantization performance on your data. Nothing ships without passing the eval.
Fine-tuning
Domain-corpus fine-tuning on dense and MoE models — LoRA, QLoRA, or full-parameter, selected by your eval results.
Model lifecycle
Versioning, A/B deployment, rollback, retirement — under change management, with the same release discipline as your regulated production systems.
Compliance
ISO 27001 and PCI DSS in place. DORA-aligned operations. EU AI Act human-in-the-loop by default.
Fine-tuning, not RAG alone
Retrieval is rent. Fine-tuning is capex.
RAG pays for context retrieval on every query — for the life of the system. Fine-tuning pays once, encoding the knowledge into the weights. Every later query runs lighter.
Domain language belongs in the weights, not the prompt.
Your terminology, document conventions, regulatory references, and decision precedent aren't facts to retrieve — they're how the model should think. RAG fetches them on every query; fine-tuning makes them native.
Sovereignty extends to the training process.
Fine-tuning runs inside your perimeter — your hardware, your data, your labels. The trained model is yours, and it never leaves. Hosted-inference vendors cannot make that claim.
Fine-tuning and retrieval compound.
Fine-tuning encodes the durable layer — terminology, judgment, patterns. Light retrieval handles the volatile parts — yesterday's policy update, today's customer record. The two together outperform either alone at enterprise scale.
Considering Sovereign AI inside your perimeter?