A private LLM, self-hosted on infrastructure you own.
Run AI on servers you control. A gateway and retrieval-augmented generation (RAG) keep customer PII off public cloud models, and every answer is grounded in your own data instead of an AI's memory. Sensitive work runs on a self-hosted model, and you can still route non-sensitive requests to a public one when that pays off.
What a private, self-hosted LLM does.
A private LLM is an AI model that runs on infrastructure you own rather than a public cloud. We put a gateway in front of it and use retrieval-augmented generation (RAG) so every answer is grounded in your own documents. Sensitive prompts stay on a self-hosted model, customer PII never reaches a third-party API, and you get PDPL-compliant AI without giving up capability.
Runs on your server
An open model such as Ollama deployed on hardware you own, so prompts and data stay inside your environment.
One controlled door
Every request passes through a gateway that decides where it goes and strips PII before anything external is ever called.
Grounded in your data
Retrieval-augmented generation pulls the right passages from your own documents so answers cite your sources and stay current.
Public only when safe
Non-sensitive requests can still go to a public model when that pays off, decided on the server and never by the user.
Private, self-hosted AI vs a public cloud API.
Send data to a public cloud AI API and every prompt and file leaves your control, lands on the vendor's servers, and may help train their next model. A self-hosted private LLM keeps that data in the UAE on hardware you own, grounds answers in your files, and bills you nothing per token for the sensitive work. For a regulated business, that reshapes both the risk and the long-run cost.
| Self-hosted private LLM (JMJ) | Public cloud AI API (ChatGPT / Gemini) | |
|---|---|---|
| Where your data lives | Your own server, in the UAE | The vendor's cloud, often overseas |
| PDPL data residency | Straightforward | Needs review |
| Does your data train their model | Never, it stays with you | Possible under their terms |
| Grounding in your own data | Every answer, via RAG with citations | From the model's memory |
| Cost model | Run it on hardware you own, no per-token bill | Per token, every call, forever |
| Lock-in | You own the model & data | Tied to the vendor's API |
Where a private LLM earns its place.
The same private AI stack covers internal assistants, customer-facing products and on-prem LLM work for regulated data. Here is where clients put it to work.
Internal knowledge assistant
Ask questions across your SOPs, contracts and reports and get answers grounded in the actual documents, with links back to the source.
Support draft assistant
Draft replies from your own policies and past tickets so agents move faster while every answer stays on-message.
Private document Q&A
Query PDFs, spreadsheets and email in one place on a self-hosted model, so nothing sensitive is uploaded to a public tool.
Dual-tier product gateway
Serve one AI feature from two models: a self-hosted tier for most users and a cloud tier for paid users, resolved server-side.
On-prem LLM for regulated data
Run the whole stack on-premise with audit logging and role-based access, where the data can never leave the building.
PII redaction gateway
Detect and remove personal data before any external model is called, so public APIs only ever see safe, scrubbed text.
Running this in production today.
Each of these is a real, anonymized build running in production.
Dual-tier AI gateway
Our own product routes AI by tier: free users are served by a self-hosted Ollama model and PRO users by a cloud model, resolved on the server so the client never chooses.
Private assistant on Ollama
A privacy-first document toolkit runs its assistant on a self-hosted Ollama model, so a user's files are answered locally and never sent to a public API.
Compliance-grade AI
A banking compliance voice agent for a regulated UAE bank, built PDPL-strict with append-only audit logging, shows the same stack holds up under real scrutiny.
Ship early, own the whole stack.
No multi-month builds and no per-token surprise bills. You see a working assistant early, and you own the model, the data and the code from day one.
Free audit
We map the data you want AI to touch, its sensitivity and where it lives, then scope the private LLM that pays back fastest. You keep the plan either way.
Fixed-scope build
A working assistant, gateway and RAG pipeline shipped at a price agreed up front. Typical audit-to-live is around 14 days for a first version.
Run & extend (optional)
Hosting, model tuning and new features on a monthly retainer, only if you want us to keep running and improving it.
Private LLM & RAG FAQ.
What is a private, self-hosted LLM?
A private, self-hosted LLM is an AI model that runs on infrastructure you own instead of a vendor's cloud. We deploy an open model such as Ollama on your server, put a gateway in front of it, and ground its answers in your own documents through retrieval-augmented generation (RAG). Your prompts and customer data never leave your environment or get sent to a public API.
Private self-hosted LLM vs sending data to a public AI API like ChatGPT or Gemini?
With a public API, every prompt and document you send leaves your control and sits on the vendor's servers, and their terms may allow your data to train future models. A private, self-hosted LLM keeps that data on your own infrastructure in the UAE, grounds answers in your files through RAG, and carries no per-token bill for sensitive work. For regulated data, the private route is usually the safer call.
Is a self-hosted LLM PDPL-compliant, and where does my data go?
Your prompts, documents and model all sit on infrastructure you own, in the UAE by default, so personal data never has to leave your servers or reach a third-party cloud model. That data residency, together with role-based access, audit logging and a gateway that strips or blocks PII before anything external is called, is what makes meeting the UAE Personal Data Protection Law (PDPL) straightforward.
What is retrieval-augmented generation (RAG) and why does it matter?
Retrieval-augmented generation (RAG) means the model answers from your own data rather than its training memory. When someone asks a question, the system searches your documents, pulls the relevant passages, and hands them to the model as context. You get answers grounded in your files with citations back to the source, which cuts made-up replies and keeps the assistant current as your content changes.
Can you route some requests to a public model and keep the rest private?
Yes. A gateway decides per request where it goes. Sensitive prompts run on your self-hosted model, while non-sensitive or public-facing ones can be routed to a cloud model when that makes sense. LifeShift 360 works this way: free users are served by a self-hosted Ollama model and PRO users by a cloud model, with the routing resolved on the server so the client never picks.
How much does a private LLM project cost, and how long does it take?
Every engagement starts with a free audit. From there most clients begin with a fixed-scope sprint priced up front, and can continue on a monthly retainer for hosting, tuning and new features. Because the model runs on hardware you own, there are no per-seat or per-token licence fees. Typical audit-to-live for a first working assistant is around 14 days.
Put AI on your data without giving it away.
Tell us what data you want AI to help with and where it currently has to leave your walls. We'll map it and show you what a private, self-hosted LLM with RAG would look like, at no cost.