"We'd love something like ChatGPT, but we can't upload company files to outside servers." We hear this all the time. The more a company handles material that must not leak — contracts, design drawings, customer data, internal reports — the more its AI adoption tends to stall.
The answer here is on-premise AI. Put simply, the AI is installed on computers inside your company, so neither questions nor documents ever leave.
1. How it differs from cloud AI
| Cloud AI | On-premise AI | |
|---|---|---|
| Where data is processed | The AI vendor's servers | Your own servers |
| Upfront cost | Low (pay as you go) | Hardware cost up front |
| Security & compliance | Managed by contract and settings | Data never leaves |
| Model performance | Latest large models | Models chosen to fit the hardware |
Neither is better in every case. The deciding factor is how sensitive the data is. Many companies split the two: non-sensitive work in the cloud, sensitive work on their own servers.
2. What an on-premise AI setup looks like
We design it in four layers. When each layer has a clear job, it's easier to fix and extend later.
- AI hardware — A server with chips built for AI (NPU or GPU) sits in your company. The language model runs here.
- Internal document search (RAG) — Company documents are indexed in advance; when a question comes in, the relevant parts are found and handed to the AI as evidence. It's what makes the AI answer from your documents instead of making things up.
- System connections (MCP) — ERP, groupware and public data are connected so the AI can only look up or enter data in approved ways.
- Business agents — These combine the tools above to handle tasks step by step, such as "summarize last month's quotes that haven't been answered."
What matters more than hardware or technology names is "which work gets faster, and by how much." That's why we always pick one task first and design backwards from it.
3. What to check before adopting
- Which material must never leave (contracts, customer data, drawings, etc.)
- The first task for AI (frequent, with material available)
- How many people will use it at once (this sizes the hardware)
- Which internal systems need connecting (ERP, email, file servers)
- Who will manage it (updates and adding documents)
4. Starting small
There's no need to roll it out company-wide on day one. We recommend putting one task on one machine, using it for real, and adding documents and tasks once the impact is clear. As documents grow, the search index needs refreshing and the model needs updates — ongoing care is part of the plan.
SOL LABS handles everything from hardware selection and document search to system integration and ongoing operations as one team. Our AI Readiness Assessment shows which setup fits your data.
In a one-hour AX consultation we map AI candidates and similar cases for you.
We build web apps, mobile apps and business systems, then add and run the AI they need. We build and operate our own meeting notes, contract and company analysis AI.
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