Your own AIrunning on your infrastructure.
We set up local LLMs on your servers or a private cloud you control. Your team gets AI; your data never leaves.
Data stays in
Prompts and files never reach a public API.
Predictable cost
Fixed infrastructure instead of a per-token bill.
No lock-in
You own the setup and choose the models.
Is local right for you?
Local wins when
- Your data is sensitive or regulated
- Your team uses AI heavily, every day
- IT has to sign off on where data goes
Tradeoffs
- Hardware or private cloud costs up front
- Open models trail the top online ones on the hardest tasks
- Someone has to maintain it (we set it up and hand it off)
The detail IT will ask for: Security and data
Where most teams start
Documents. Invoices, contracts and claims are high-volume and sensitive, which is where local pays off fastest.
How we set it up
Step 1
Assess
Your use cases, data and hardware.
Step 2
Test
Candidate models on your real tasks.
Step 3
Recommend
Local, hybrid, or stay hosted.
Step 4
Deploy and hand off
Running on your infrastructure, your team trained.
Local isn't always cheaper. We measure before we recommend it. How we work out the break-even
Need a stronger model sometimes? A local check comes first.
- 01
Check locally
Personal data and policy checks run on your side first.
- 02
Mask or block
What must not leave is removed, with a reason IT can audit.
- 03
Then send, if allowed
Only the cleaned request goes to the online model.
- PassSupplier price list, 3 pagesNo personal data · public terms
- RedactCustomer complaint emailName, phone, and account number masked before send
- BlockEmployment contract draftPolicy: HR documents never leave
Only the allowed payload continues to the external model
A demo and pilot capability today, not a certified product.
Ask for the gateway walkthrough on the discovery callFind out if local AI fits your company.
We'll tell you whether local, hybrid or hosted is the right answer.
Book a discovery callOr write to hola@aekora.com
For ITModels we evaluate
The list changes; the method doesn't.
| Job | Examples we may evaluate |
|---|---|
| General assistants and chat | Qwen3, Gemma 4, Llama 4 Scout*, Mistral, sized to your hardware |
| Documents and OCR | PaddleOCR-VL, DeepSeek-OCR, Granite-Docling, Mistral OCR, Qianfan-OCR |
* Its community license restricts some rights depending on where an organization is domiciled. We verify the current terms before recommending it.
From the blog
Further reading
- 29 October 2026 · Local vs cloudThe state of local document AI in Europe, 2026: models, prices, regulation and what buyers doThe state of local document AI in Europe 2026: compact open-weight models, the GPU vs API crossover, EU AI Act and GDPR pressure, and how EU buyers decide.Read the article
- 13 October 2026 · Model landscapeHow to benchmark OCR models on your own documents: a 200-page gold set, a fair harness, and the metrics that map to moneyHow to benchmark OCR models on your own documents: a 200-page gold set, per-field accuracy, straight-through rate, and a fair local-vs-API comparison.Read the article
- 1 October 2026 · GDPR & EU AI ActWhat is data sovereignty in AI? Residency, localization and control, explained for European teamsAI data sovereignty explained for Europe: how it differs from residency and localization, GDPR, Schrems II and the CLOUD Act, and a vendor checklist.Read the article