cashcrown // AI infrastructure

Sovereign AI infrastructure

Your models, on your hardware. Predictable cost instead of a surprise bill.

Dependency on a single API provider is a silent risk: costs scale with traffic, data leaves your environment, and migration means rewriting integrations. Sovereignty flips this relationship — the system must allow you to change providers, never the other way around.

We deploy LLM serving (vLLM, Ollama), embedding servers (BGE-M3), private "Company GPT," and RAG on corporate knowledge, all fronted by a router/gateway that standardizes input and controls cost. You don’t need a GPU cluster upfront — we tailor the setup to your actual workload. Compliance is designed from the start, and PII is masked before any data leaves for the cloud.

// 01Problem

Dependence on a single API provider is a risk: cost grows with traffic, data leaves your premises, and switching providers means rewriting integrations. You lack control over the model, latency and privacy.

// 02Approach

We stand models up locally or in your cloud: LLM serving, an embeddings server, a private „Company GPT”, RAG over company knowledge. We design so that you can switch provider — never the other way around. The router unifies access and controls cost.

self-hosted LLMOllamavLLMBGE-M3QdrantNSSM / systemd

// 03Process

Sizing and cost
Choosing models and hardware to fit real load and budget.
On-prem deployment
LLM serving + embeddings, fronted by a router/gateway.
RAG over knowledge
Document indexing, semantic search, answers with citations.
Hardening
Observability, backups, network isolation, cost control.

// 04What you can build

cashcrown@lab: infrastruktura --listready

self-hosted LLM — deployment of local models
private company ChatGPT — on-prem, your data stays with you
embeddings server — semantic search engine
RAG over company knowledge — answers from your documents
AI gateway / router — multi-model, fallback, cost control

// 05Examples: how we build it

Ready systems in this area — from measured models and components, to try live:

Real-time monitoring and alertsEvent streams, anomaly classification and alerts — before a problem grows, not after the fact.

// 06FAQ

Do we need our own GPUs?

Not necessarily. We pick a variant to match the load — from small models on a CPU/single GPU up to a cluster. What matters is predictable cost, not maximum hardware.

How does this relate to OpenAI/Anthropic?

The router lets you mix: local models for sensitive paths, the cloud where you need raw power. No lock-in.

Does data leave the company?

In the on-prem variant — no. We mask PII before anything leaves for the cloud.

How much does our own AI infrastructure cost?

It depends on the variant — from small models on a CPU to a cluster. We aim for predictable monthly cost, not maximum hardware; local can be cheaper and safer than an API at steady, high traffic. Compare local vs cloud in the inference calculator, and we start with a fixed-cost pilot.

Is this compliant with the AI Act and GDPR?

Yes. Self-hosting and PII masking keep sensitive data local (it can stay in-country), and the router gives you an audit trail. We design transparency and human oversight in from the start; profiling or decisions about people add a DPIA.

// →Related

Services in this domain

Product: BGE-M3 Search

Case studies

Let's start with an audit and a pilot.

We show a working system before we ask for your trust.

Book a call

Sovereign AI infrastructure

Your models, on your hardware. Predictable cost instead of a surprise bill.

// 01Problem

// 02Approach

self-hosted LLMOllamavLLMBGE-M3QdrantNSSM / systemd

// 03Process

Sizing and cost
Choosing models and hardware to fit real load and budget.
On-prem deployment
LLM serving + embeddings, fronted by a router/gateway.
RAG over knowledge
Document indexing, semantic search, answers with citations.
Hardening
Observability, backups, network isolation, cost control.

// 04What you can build

cashcrown@lab: infrastruktura --listready

self-hosted LLM — deployment of local models
private company ChatGPT — on-prem, your data stays with you
embeddings server — semantic search engine
RAG over company knowledge — answers from your documents
AI gateway / router — multi-model, fallback, cost control

// 06FAQ

Do we need our own GPUs?

Not necessarily. We pick a variant to match the load — from small models on a CPU/single GPU up to a cluster. What matters is predictable cost, not maximum hardware.

How does this relate to OpenAI/Anthropic?

The router lets you mix: local models for sensitive paths, the cloud where you need raw power. No lock-in.

Does data leave the company?

In the on-prem variant — no. We mask PII before anything leaves for the cloud.

How much does our own AI infrastructure cost?

Is this compliant with the AI Act and GDPR?

Let's start with an audit and a pilot.

We show a working system before we ask for your trust.

Book a call