Data sovereignty
Your content stays on your network. No provider reads along, no training on your data.
New topic
Local AI
Language models on your own hardware: confidential, predictable and under your control.
Internal assistant
Local · no cloud
What is the deadline for complaints?
Assistant · local
Complaints must be reported within 14 days of goods receipt. The quality management team is responsible.
QM-Manual.pdf · ch. 7.3
Model: local
Data transfer: 0 KB
Local AI means the language model runs on a server or workstation inside your company – not at a provider in the cloud. Your documents, queries and answers stay on your own network.
That is not an end in itself. It is the difference between ‘let's try some AI’ and an assistant you can trust with HR files, engineering drawings or client data.
What makes local AI different
With cloud services, your input leaves the company. Depending on the provider, content is stored, analysed or used to improve models. Contracts can restrict this, but technology cannot guarantee it. Anyone working with sensitive data remains dependent on promises.
Locally, you reverse that: the model is a file on your hardware. You decide which documents it reads, who uses it and when it is switched off. Updates, logs and access remain in your hands – verifiable for data protection and audits.
And the cost?
Cloud AI is billed per token. That is convenient, but hard to plan once many employees use it regularly. Local AI requires an investment in hardware and support. Beyond a certain usage level it is cheaper – especially if the alternative is paying for every query. We calculate both variants in advance, using your actual usage figures.
Benefits
Six reasons for local AI
Not every advantage carries the same weight. These six points decide in practice.
Predictable costs
A one-off investment instead of variable token bills. Support costs are calculable.
Resilient offline
Keeps working during internet outages – important for production, labs and field service.
Your own knowledge
Answers from your manuals, contracts and wikis – including the source reference.
No dependency
Model and data stay with you. You can change provider at any time.
Traceable
Prompts, answers and access can be logged and reviewed.
DocumentsManuals, contracts, wikis
internal
IndexEmbeddings with permissions
searchable
ModelLlama, Mistral, Qwen – local
offline
AnswerWith source and approval
checked
No step leaves your network.
Architecture
How it works
A local assistant is not a single program but a stack of four parts. Every part runs on your network – from document to answer.
Typical use cases
Not every task needs a frontier model. These cases work particularly well locally because the data is confidential and the answers come from known material:
- Questions about manuals, contracts and policies
- Drafting quotes, emails and minutes
- Transcribing and summarising meetings
- Internal knowledge base and support triage
- Code and script assistance for IT
- Sensitive areas: HR, legal, engineering
In all cases the rule is: the model drafts, a person approves. A local assistant does not replace sign-off, it speeds it up.
Cloud AI or local AI?
The honest answer: it depends on the task. This comparison helps with the decision.
| Criterion | Cloud AI | Local AI |
|---|---|---|
| Data processing | At an external provider | On your hardware |
| Cost | Subscription or per token | Hardware plus ongoing support |
| Internet | Mandatory | Not required |
| Customisation | Limited, mostly via prompts | Your documents, your models |
| Control | Provider-dependent | Entirely with you |
| Quality | Very high (frontier models) | High – depending on model and hardware |
From first conversation to assistant
We start small and measurable – not with a large platform nobody uses.
-
01
First conversation
Use case, data types, expectations and constraints.
-
02
Feasibility
Model, hardware, effort and cost – with a clear recommendation.
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03
Pilot
One use case with real documents and measurable results.
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04
Integration
Connection to file shares, Outlook, Teams or an internal web interface.
-
05
Operation
Updates, monitoring, backups and user training.
What local AI cannot do
Local AI is not magic. Anyone replacing a cloud solution should know the limits:
- Hardware costs money. Useful models need a GPU with sufficient VRAM – depending on model size and number of users.
- Smaller models are not always as good. Often sufficient for standard tasks, not for complex reasoning.
- Operation means maintenance. Updates, monitoring, backups and permissions must be managed.
- Not every idea pays off. We assess the benefit before the investment.
If local is not sensible, we say so – and propose a hybrid approach: sensitive data locally, non-critical tasks in the cloud.
Local AI
Your data. Your model. Your decision.
We advise independently and tell you whether local is the right choice for your use case.