Powerful AI. Inside your infrastructure.
Private AI without sending confidential data to a public model. We design, build and support AI environments that run in your private cloud, on your premises, or fully air-gapped — sized to your workload and your risk profile.
- From €20,000 — scope depends on hardware, integrations and support.
- An NDA can be arranged before technical discovery.
Four deployment models.
One honest comparison.
Switch modes to see where data travels and which external connections remain. Not every deployment needs to be air-gapped — and no architecture removes every risk.
On-premise. Inference, retrieval and orchestration run on hardware in your own facility. No prompts or documents leave the building. Remote model APIs are not required.
A complete private AI stack.
Not a model on a server.
- Local inference — open-weight models served on your hardware (e.g. via Ollama or llama.cpp), selected and evaluated against your actual tasks.
- Private RAG — your documents indexed with permission-aware retrieval, so answers cite sources and respect who may see what.
- Document indexing — contracts, wikis, drives, and databases connected with incremental updates.
- Internal AI APIs — one governed endpoint your tools and workflows call, instead of ad-hoc integrations.
- Access control — role-based permissions, encrypted secrets, and tenant separation where needed.
- Monitoring & audit — usage, latency, quality signals, and a complete audit log.
- Backup & maintenance strategy — model updates, index rebuilds, and recovery planned from day one.
Hardware and model selection, from evidence.
Sizing local AI is an engineering decision, not a shopping trip. Token throughput, context length, concurrency and quantization interact — and the difference between a right-sized and an oversized deployment is often tens of thousands of euros.
We benchmark candidate models on representative tasks before recommending hardware. Our methodology is public: see the Strix Halo Local AI Guide, our open research on deploying and benchmarking LLMs locally.
Who this is for
Legal and professional services, healthcare-adjacent administration, finance, R&D-heavy manufacturers, public-sector suppliers, and any organization whose contracts, records or IP should not transit a public AI API.
Privacy is an architectural choice — not a checkbox on a vendor's website.
From requirements to a running private stack.
Requirements
Data classification, workloads, users, performance targets, compliance context. NDA first if you prefer.
Architecture
Deployment model, network design, model shortlist, hardware sizing — documented and reviewed with your IT.
Evaluation
Candidate models benchmarked on your representative tasks. Evidence, not vendor claims, drives selection.
Build & integrate
Inference environment, RAG index, access control, internal API, and connections to your systems.
Operate
Handover with documentation — plus optional ongoing operations: updates, evaluations, and expansion.
Private AI deployments typically start around €20,000, depending on hardware, integrations, security requirements, and support. We scope precisely after requirements — never from a price list.
Keep sensitive AI workloads inside your own infrastructure.
Tell us what may never leave the building. We will design the architecture around that constraint — under NDA if you prefer.