Your teams are already using artificial intelligence. The question is not whether they will, but where the data ends up when they do. We deploy language models on your own infrastructure, fed with your documentation, so the information never leaves your network.
The real problem is not AI. It is the leak
When an organisation bans public chatbots by internal memo, the usage does not disappear: it goes invisible. Staff keep pasting contracts, proprietary code, forensic reports or patient data into third-party tools, now with no record and no control.
That leaves two holes open at once. A confidentiality one, because the material leaves the perimeter. And a compliance one, because in many cases there is an international transfer and a processor with no Article 28 GDPR contract behind it.
The alternative that works is not banning it better. It is providing an approved tool that is just as convenient, running where you are in charge.
Why on-premise rather than against an API
Confidentiality
The data never crosses the perimeter
The model runs on your hardware. Nothing is sent to third parties, no provider retains it and none of your information trains anything.
Compliance
Several problems disappear at once
No international transfer, no external processor to contract and, under DORA, no third-party ICT provider to register and plan an exit from.
Control
Your model does not change under your feet
A cloud provider updates or retires models whenever it likes. On-premise, the version is frozen until you decide otherwise, and results stay reproducible.
On top of that sits something prosaic but decisive once volume appears: cost stops being a variable per-token invoice and becomes an amortisable investment with a known power draw.
What we actually deploy
Open models running on your hardware. Selected for your use case and for the size your infrastructure can serve comfortably, not the largest one that fits.
RAG over your documentation. The model answers citing your procedures, contracts, internal policy or technical history, instead of improvising from what it memorised in training.
Access control wired to your directory. Who may ask what, and over which corpus. An intern and the board do not see the same documentation.
Full traceability. A record of queries and answers, which is exactly what you will be asked for when governance has to be demonstrated.
A hardened platform. Plus an offensive verification on delivery, because a badly exposed AI server is a new door into your network.
How you get in: four steps
You do not have to commit an investment on day one. The ladder is built so that you decide on your own data, not on a sales promise.
1
Feasibility assessment
One or two days. Inventory of real use cases, classification of the data they will touch, indicative sizing and a written recommendation. Including telling you it is not worth it, if that is the answer.
1-2 days
2
Proof of concept
We build your case on our lab, with your real documentation under NDA. You try it before buying a single piece of hardware.
2-3 weeks
3
Deployment on your infrastructure
Final sizing, hardened installation, models, RAG, identity integration, traceability and a security verification of the platform.
Project
4
Ongoing operation
Model updates, corpus reindexing, answer quality control, log review and periodic prompt injection testing.
Monthly
The three services
No hardware purchaseProof of conceptYour documentation, our lab. A working demo and a report with quality measured on your own data.View service →In your server roomOn-premise deploymentSizing, hardened installation, models, RAG and directory integration. Documented and audited on delivery.View service →AI Red TeamAI security auditPrompt injection, RAG exfiltration and corpus poisoning. Against the OWASP Top 10 for LLM Applications.View service →
What others will not tell you
We would rather you knew before signing than three months afterwards.
A local model does not match the best commercial model on the market. On complex reasoning there is a gap. For searching, summarising, drafting and answering over your documentation, the gap stops mattering. Choosing the right use case is half the project.
The bottleneck is usually memory bandwidth, not raw compute. A machine with plenty of unified memory but little bandwidth loads huge models and serves them slowly. Sizing for real concurrency is what separates a useful deployment from a frustrating one.
RAG is not magic. Answer quality depends on how your documentation is chunked and indexed. A messy corpus produces messy results. That part is work, and we quote it as such.
It has to be operated. An AI server is not a household appliance: models evolve, the corpus changes and answers degrade if nobody measures them.
Who should be considering this
Law firms and advisories — professional privilege does not allow a case file to travel to a third party.
Healthcare — health data, a special category under Article 9 GDPR.
Industry and defence — technical documentation, drawings and material subject to classification or to end-customer agreements.
Banking, insurance and DORA-regulated entities — every external API is a third-party ICT provider with obligations attached.
Public sector and its suppliers — the ENS does not go away because the tool is new.
Frequently asked questions
How much hardware is needed?
It depends on two things: model size and how many people will use it at the same time. A small team with a narrow case is solved with a single professional card; a departmental deployment with dozens of concurrent users needs something else. We size it during the assessment, with your real user numbers, and not before.
Do you sell the hardware?
We can handle procurement or work on whatever you buy yourself. We do not depend on a brand and we do not live off the equipment margin, which leaves us free to recommend honest sizing rather than the most expensive one.
Can we start small?
That is what we recommend. One concrete, measurable use case with a small group of users. If it works and gets used, it grows. Most failed AI projects failed by starting too ambitious.
What if our documentation is a mess?
That is the norm, and it surfaces during the proof of concept before any investment is committed. Preparing the corpus is part of the work. Less glamorous than the model, but it is what determines whether the answers are useful.
Does this replace ChatGPT or Copilot for everything?
No, and anyone promising that is selling smoke. It replaces the usage that should not be leaving your network today. For the rest it can coexist perfectly well with commercial tools, as long as they are approved and governed.
Start by finding out whether it pays off
Half an hour to review which use cases you actually have, what data they touch and whether your volume justifies your own infrastructure. If the answer is no, we will say so. No commitment and no forms.
Phone: 686 250 244 · Email: info@jaymonsecurity.com
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