Guide: Local AI for businesses
What local AI is, when it beats cloud AI, what you need to implement it and how to measure the return. A practical guide for SMEs that cannot send their data outside the company.
1. What local AI is (and what it is not)
Local AI is a physical server, installed at your company, that runs artificial intelligence models without sending your queries to any provider. The models, your documents, the instructions you give it and the agents that work for you live on that machine. The only things that leave your network are the integrations you switch on: email, WhatsApp, your ERP. Everything else stays inside.
It is not a computer with a chat app installed, and it is not “private” access to a cloud service.
2. When it makes more sense than cloud AI
Cloud AI (ChatGPT, Copilot, Gemini and the like) is convenient to get started. It stops paying off as soon as any of these apply:
- You pay per user and per use. Ten people at €25 a month plus API credits puts you over €3,000 a year without having automated anything. Another invoice, another month, forever.
- You handle data that cannot leave the company. Contracts, payroll, drawings, customer records, defence or public administration information. Whatever you send to the cloud leaves your company and sits under someone else’s terms.
- You need agents that do tasks. Classifying invoices, reconciling accounts or drafting proposals takes several model calls per task. In the cloud, every call is tokens you pay for. Locally there is no per-token fee. You pay the electricity the box uses and the support you ask for. Nothing else.
- You want AI that works your way. A service shared by millions does not keep your context or follow your instructions. On your own server you choose the models, the instructions and the permissions.
3. What you need to implement it
- Privaia Secia. You don’t need a 5 kW rack. A quiet, low-power desktop microserver covers you from 5 users up. More nodes mean more capacity and redundancy.
- Training on one concrete process. The usual mistake is “we want AI”. What works is “we want to stop keying in invoices” or “we want to find any contract in 10 seconds”. The training hours go into that.
- Acceptance and success criteria. Before installing anything, define what counts as a correct result and how you will check it.
- Human review. Decide who can ask for what, and which actions (sending, paying, publishing) need a person’s approval. The AI prepares, a person approves.
- Adoption time. People drive adoption, not hardware. Someone on your team has to actually use it.
4. How it is implemented: the pilot, month by month
One process, one node on your network and your own data. This is how it goes:
| Month | What happens | What you get |
|---|---|---|
| 1 | Assessment: process, test data, criteria, current AI SaaS cost | A measurable plan |
| 2 | Node installation on your network, company instructions, permissions, first agent workflow | AI working on your use case |
| 3 | Real use with your users, debugging, review of local logs | An automated process your team uses |
| 4 | Review of results and costs, configuration handover, support agreement | A purchase decision based on data, not promises |
5. How to measure the return
The return on local AI is not worked out from productivity estimates. It is worked out from invoices and measured tasks:
- Replaced spending: the cloud AI seats, credits and APIs you stop paying for.
- Solution cost: the node, the implementation, the electricity and the support hours you ask for.
- Hours recovered: tasks a person used to do end to end and now reviews in a minute. Count them separately from cash savings: they are time your team now spends on something else.
One node, €4,000 + VAT. Two nodes, €10,000. Three nodes, €13,000. No PRIVAIA fee or subscription, no per-user or per-token charge. Services and support at €150/hour + VAT, only when you ask for them. The more real work moves to the AI, the sooner the node pays for itself. From then on, every month without a subscription is savings. Run the numbers with your own figures.
6. Mistakes that cost you money
- Starting with chat. Local chat is useful, but the value lies in agents that carry out complete tasks.
- Giving access to everything from day one. Start with one folder, one process and one group of users.
- Skipping human review. AI prepares; a person approves. Especially when sending, paying or publishing.
- Not logging what it does. Without local logs, there is no audit and no improvement.
- Confusing “local” with “maintenance-free”. Updates, backups and reviews still happen. They happen offline and through tickets, but they happen.
7. Checklist before deciding
- I know how much I currently pay for cloud AI (seats, credits, APIs).
- I have identified a repetitive process with test data.
- I know which data cannot leave the company.
- I have decided who approves actions with external effects.
- I have someone responsible for internal adoption.
- I have defined how I will measure the result in the first month.
If you have checked four or more, you are ready to run a pilot.
Buy it. It is yours from day one.
From €4,000 + VAT. One node in your office, your data inside and no per-user cost. Tell us what work you want to automate and we tell you configuration, delivery and costs before we start.
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