Private Enterprise LLM And AI Agent Development
Retrieval systems, agentic workflows, and model fine-tuning built to run inside your infrastructure. Open-weight models run on your hardware or ours by default, and your own cloud model account can be connected for the tasks your policy allows.
WHAT WE BUILD
Core LLM & AI Agentic Capabilities
Private RAG Systems
Document ingestion, chunking, embedding, and retrieval over your own corpus. The vector store sits on your infrastructure. No document reaches an external API at any point in the pipeline.
Agentic Workflows
Multi-step agents that read, decide, and act against your systems, with a human approval gate on every write operation. Built for the processes that currently consume a person’s afternoon.
Fine-Tuning On Customer Infrastructure
Domain adaptation on your data, on your hardware. Your acronyms, your document taxonomy, your schema formats. The training data and the resulting model stay with you.
MCP Integration
Model Context Protocol connectors between language models and the systems you already run, ERP, CRM, ticketing, document management. Including for models and hardware we did not supply.
How We Build Private LLM And Agent Systems
Open Weights By Default, Your Own Cloud Account By Choice
We deploy open-weight models under permissive licences, selected for each workload, so the core of the system carries no repricing risk and no model deprecated out from under it. Where your policy allows a task to use a frontier model, the system connects to your own Claude, Gemini or OpenAI account. ARSA never hosts or proxies that model: the account, the contract and the data terms stay yours.
Approval Gates On Every Write
Agents prepare transactions; a person commits them. This is architectural in everything we build and cannot be switched off under delivery pressure.
Deployed Where Your Data Is Allowed To Be
On-premise, air-gapped, or single-tenant hosted in a region you choose. The deployment target is a design input from the first week.
Built By A Team That Ships AI Into Regulated Environments
ARSA has been building and operating AI systems in production since 2018, across defence, law enforcement, and industrial deployments where the system has to keep working and the data cannot leave the building. The same engineers build the language-model systems described on this page.
Every engagement starts with a paid feasibility assessment: an operational diagnosis, a technical feasibility judgement against your actual data and infrastructure, and a cost model. Two weeks. We will tell you if it should not be built.
TIERS
LLM Project Stages And Timelines
From First Conversation To Production
- Feasibility assessment 2 weeks
- Pilot deployment 8 weeks
- Production programme 12 weeks and more
| Stage | From | Duration |
|---|---|---|
| Feasibility assessment | $4,500 | 2 weeks |
| Pilot deployment | $20,000 | 8 weeks |
| Production programme | $60,000 | 12 weeks+ |
The feasibility assessment fee is deducted from the project fee if you contract within 90 days.
FAQ
AI LLM & RAG Service Questions
Which models do you deploy?
Open-weight models under permissive licences, selected per workload, currently the Qwen and Llama families for general reasoning, plus specialist models for OCR and embedding. Model choice is a design decision made in the assessment, workload by workload. Your own Claude, Gemini or OpenAI account can be connected for the tasks your policy allows.
Does our data leave our infrastructure?
Not by default. Retrieval, inference, and fine-tuning run on infrastructure you control, and where we host on your behalf it is a single-tenant instance in a region you choose. Text reaches a cloud model only if you connect your own Claude, Gemini or OpenAI account and your policy allows that task to use it. ARSA never hosts or proxies the frontier model.
Who owns a fine-tuned model?
Your data remains yours in every case. A model adapted exclusively on your data for your use case is typically yours to use; the base weights remain under their original open-weight licence.
Can you integrate with systems we already run?
Yes. MCP connectors for ERP, CRM, ticketing, and document systems, including where the model and the hardware were not supplied by ARSA.
How do you stop an agent doing something irreversible?
Hard-coded human-in-the-loop approval on every write operation. Agents prepare transactions; a person commits them. It is part of the architecture and cannot be turned off.
What if we already run models on cloud APIs?
Keep them for the tasks where they work. The system can call your own Claude, Gemini or OpenAI account for the tasks your policy allows, while open-weight models handle the rest on your infrastructure. Data residency, cost at scale, and vendor deprecation risk are the usual reasons to move a task local; if none of them applies, the cloud model is a reasonable place for that task to stay, and we will say so. ARSA never hosts or proxies the frontier model.
Find Out Whether Your LLM Project Should Be Built
In two weeks the feasibility assessment gives you an operational diagnosis, a technical feasibility judgement against your own data and infrastructure, and a cost model, in writing. If the answer is that the system should not be built, you hear that too.
Everything we build runs on infrastructure you control and can run air-gapped, with no internet connection. Your documents reach a cloud model only through your own account, for the tasks your policy allows, and a model tuned on your data stays with you. For document work that should start without a build project, ARSA Sovereign packages the same approach as an on-premise appliance. The same engineers have shipped AI that runs entirely on the customer’s side, from embedded speech recognition to restricted-area security for the Ministry of Defense.
Related reading: why AI agents get facts wrong and how knowledge graphs fix it, audit trails and human approval for agents, connecting agents to internal systems on-premise and on-premise agent cost against a SaaS subscription.