10ⁿ Tech builds self-hosted, sovereign enterprise AI platforms – your own fine-tuned large language model running on your own GPU server, in your own country, grounded in your own data, with the intelligence never leaving your control. You own the model, the code, the data and the outputs. No per-seat SaaS fees, no third-party dependency, no cloud lock-in.
What is self-hosted enterprise AI?
Most “enterprise AI” today means sending your prompts and documents to a third-party SaaS model you don’t control, in a jurisdiction you may not be allowed to use. Self-hosted enterprise AI inverts that: the model runs on hardware you own or host, the data stays on-premises, and production traffic never leaves the country. The platform is built on a portable, open-source application layer so nothing is locked to one vendor – you can move it to another cloud, or fully sovereign infrastructure, without re-engineering.
Why self-host your AI
Your corpus stays on-premises with the model. Only derived artifacts move, and production traffic stays in-country – the answer to data-residency and regulatory mandates.
Model, source code, data and outputs are yours. Build-for-hire, no vendor ownership, no hostage data.
An open-source stack – open model, open orchestration, open vector and graph – means consumption cost, not per-user SaaS tax that scales against you.
Containerised and portable. Every component sits behind a swappable interface, so you can move clouds or go fully sovereign later without a rebuild.
The architecture
We deploy a portable, open-source application layer: open where it carries your intellectual property (the model, orchestration, vector index and knowledge graph), and managed cloud services for commodity platform plumbing. Your fine-tuned model is served on an always-on GPU server; the platform layer runs in a cloud landing zone (e.g. Azure UAE North); the two are joined by an in-country site-to-site VPN.
Your data stays yours
The corpus stays on-premises with the model. Embedding runs on-prem; only derived vectors and the knowledge graph cross the link to the cloud platform layer – the bulk data never traverses your uplink. Production inference runs on your GPU server, in-country. A frontier model (via an OpenAI-compatible interface) is used only for benchmarking and testing, never as a production dependency. The result: a typical query moves only tens of kilobytes across the network, and sensitive data never leaves your jurisdiction.
Open, portable, and grounded
The stack is open-source where it matters: an open model (JAIS, Falcon, Qwen or similar), open orchestration (LangChain / LlamaIndex), pgvector for the index, an open graph (Neo4j Community / Apache AGE), and open optimisation tooling. Stateful core services are open; stateless peripheral inference (speech, vision, OCR) sits behind swappable managed APIs. Everything is containerised and movable to another cloud or sovereign infrastructure later, with no re-engineering and no lock-in.
What we build, what you bring
You own the intelligence – your models, data and domain expertise. We own the platform and the applied engineering that turns those models into a deployed, retrieval-grounded, explainable, secured system:
- Model serving + routing – package, serve, version and monitor your fine-tuned model behind an OpenAI-compatible API; operate the router and MLOps.
- RAG pipeline – ingestion, chunking, embedding, hybrid retrieval, reranking and retrieval evaluation.
- Knowledge graph – ontology engineering, entity extraction and resolution, graph-RAG (we build the ontology with your subject-matter experts where none exists).
- Explainable AI – feature-importance scorecards for predictive models, groundedness and citations for the generative layer, surfaced in the UI.
- Dashboard, APIs and integration – operator/admin UI, enterprise identity, and connectors to your existing data feeds and endpoints.
- Cloud, security and support – landing zone, VPN, private networking, WAF, monitoring, backup, plus admin and end-user training.
How we deliver it
We deliver in phases so value lands early and risk stays low:
- Prototype – landing zone, VPN to your GPU server, first model integrated, core RAG, a minimal ontology and a working dashboard.
- Operational MVP + pilot – expanded knowledge graph, fuller explainability, real pilot users, integrations and validation.
- Production – hardened, secured platform, training, and handover – with a documented path to expanded production or fully sovereign cloud.
Where self-hosted AI fits
- Government and public sector – decision-support and situational awareness where data residency is non-negotiable.
- Regulated enterprise – banking, healthcare, energy, defence – where prompts and documents cannot leave your control.
- Knowledge-heavy operations – grounded question-answering, document intelligence and analyst copilots over your own corpus.
- Research and national programmes – where IP ownership and portability are contractual requirements.
Why 10ⁿ Tech
We are the applied-AI engineering partner that turns your models into a deployed system – retrieval-grounded, explainable, secured and sovereign. We design, build, integrate, host and secure the platform on Azure UAE North (or your chosen cloud), with an in-country GPU server for production inference, and we initiate cloud-GPU scale-out through our Microsoft partnership when you need it. You keep the intelligence and the IP; we make it production-grade.
Frequently asked questions
What is self-hosted enterprise AI?
An AI platform where your own fine-tuned model runs on your own GPU server and your data stays on-premises, rather than calling a third-party SaaS model. It gives you data sovereignty, IP ownership and no vendor lock-in, while still delivering RAG, knowledge-graph reasoning and explainable answers.
Does my data ever leave my premises?
No. The corpus and the model stay on your GPU server; embedding happens on-prem and only derived vectors and the knowledge graph move to the cloud platform layer. Production inference and sensitive data stay in-country.
What GPU server do I need?
A server with a current data-centre GPU (for example an NVIDIA RTX PRO 6000 Blackwell, 96 GB) is enough to run a strong open model – a 70B-class model at 4-bit, or a ~30B at 8-bit, fits comfortably with headroom for the context cache.
Which models can I run?
Any open model you fine-tune – JAIS, Falcon, Qwen and similar are all supported. The platform serves them behind an OpenAI-compatible interface, so the rest of the system is model-agnostic and swappable.
Is it explainable?
Yes. Predictive models get feature-importance scorecards; the generative layer gets groundedness checks, citations and tracing. Every answer is auditable, not a black box – essential for regulated and government use.
Am I locked into one cloud?
No. The application layer is open-source and containerised, with every component behind a swappable interface. You can move to another cloud or fully sovereign infrastructure later without re-engineering.
Own your AI, in your own country
If your data can’t go to a public AI service, you don’t have to give up AI – you self-host it. Talk to us or explore the full solutions portfolio.
