On-Premise AI & Private AI for Business

AI That Never Leaves Your Building

Many organisations want what language models can do — search every document in seconds, draft and summarise, transcribe calls, answer staff questions — but cannot send client files, patient records or proprietary data to a third-party AI service. On-premise AI removes that conflict. The model runs on your hardware or in your private cloud, and the data never leaves.

Open-weight models have become capable enough that, for most document and assistant workloads, the private option is no longer the compromise it used to be.

What We Deploy

Private language models — self-hosted open-weight LLMs for drafting, summarising, classification and extraction, served behind your own API
Private document search (on-premise RAG) — ask questions across contracts, policies, case files or manuals and get answers with citations to the source document
On-premise speech recognition — transcription of calls, meetings and dictation without audio leaving your network
Local AI agents — assistants that act inside your internal systems with the same permissions model as your staff
Private coding assistants — self-hosted code models for teams that cannot send source code to an external service
Hybrid setups — sensitive workloads on private models, non-sensitive ones routed to hosted APIs where that is cheaper or stronger

Who Chooses On-Premise AI

Law firms

Privileged documents, client confidentiality and engagement terms often rule out public AI tools. A private AI deployment lets lawyers search and summarise matter files, draft from precedent and review contracts, with nothing leaving the firm.

Healthcare

Patient data under HIPAA and similar regimes. On-premise transcription, clinical-document search and administrative assistants avoid a data flow that would otherwise need a Business Associate Agreement and a risk review.

Finance, government and defence

Regulated or classified data, data-residency requirements, and environments that must run air-gapped.

Any business with valuable IP

Source code, product designs, pricing models and strategy documents that should not become someone else's training data or sit in someone else's logs.

Cloud vs On-Premise AI

	Hosted AI API	On-premise / private AI
Where data is processed	Provider's infrastructure	Your servers or private cloud
Upfront cost	None	Hardware and setup
Running cost	Per token, grows with usage	Mostly fixed once deployed
Strongest available models	Yes	Open-weight models only
Data residency and air-gap	Limited	Full control
Governance and audit	Provider's terms	Your policies, your logs

The honest answer for many organisations is hybrid: keep sensitive work private and use hosted models where the data allows it. We will recommend the split that fits your data and budget.

How We Deliver It

Classify the data and workloads. What is sensitive, what the rules require, and which tasks matter most.
Benchmark models on your tasks. Candidate open-weight models are tested on real examples from your documents, so the choice is measured.
Size and set up infrastructure. GPU servers on-site or a private-cloud tenancy, sized from expected users and latency.
Build the application layer. Document ingestion, vector search, access controls that mirror your existing permissions, audit logging, and the interface your staff will use.
Hand over and support. Documentation, monitoring, and a model-update process you control.

Related Work

Conversational AI — assistants and chatbots, which can run on private models.
AI and machine learning — custom models and ML systems.
Fine-tuning vs RAG — choosing how to adapt a model to your data.
RAG for business — how retrieval-augmented generation works in practice.

Keep Your Data. Use the AI.

Tell us what data you work with and what you want AI to do with it. We will tell you whether private AI fits, what hardware it needs, and what it would cost.

Get in Touch

Frequently asked questions

What is on-premise AI?

AI that runs on infrastructure you control — your own servers, a private data centre, or an isolated private-cloud tenancy — instead of sending data to a public AI provider's API. The models, the data and the logs all stay inside your environment.

What is the difference between private AI and public AI?

Public AI means using a provider's hosted models over the internet, where prompts and documents are processed on their infrastructure under their terms. Private AI means the model runs in an environment you control. Hosted frontier models are still ahead on the hardest reasoning tasks; open-weight models run privately are more than good enough for most document, search, extraction and assistant workloads.

Are self-hosted AI models good enough for business use?

For a large share of real workloads, yes: answering questions over internal documents, summarising, classifying, extracting fields, drafting, and transcription. We benchmark candidate models on your own tasks before recommending one, so the decision rests on measured accuracy rather than leaderboard scores.

What hardware do we need?

It depends on the model size, the number of concurrent users, and the latency you need. Small models and transcription can run on a single workstation-class GPU; larger models serving many users need dedicated GPU servers. We size the hardware from your expected load and can start with a pilot on modest hardware before you commit to more.

Is on-premise AI suitable for law firms and healthcare?

These are the most common reasons to choose it. Privileged legal documents and patient data often cannot be sent to a third-party AI service under client agreements, professional rules or HIPAA. Running the model in-house removes that data transfer entirely, which makes the compliance conversation much simpler.

Can it run fully air-gapped?

Yes. Models, embeddings, vector database and application can all be installed on a network with no internet access. Updates are delivered as signed packages you apply on your own schedule.

On-Premise AI: Private, Self-Hosted AI That Keeps Your Data In-House