Local AI — your data stays on your hardware
Start with small efficient models, scale to fine-tuned large ones. Your data never leaves your network.
The Challenge
You can't send sensitive data to OpenAI or Anthropic. Maybe it's patient data, financial records, IP, or simply a regulatory hard line. But you still want real AI capabilities — not a downgrade. Most "on-premise AI" projects collapse under their own weight because nobody started small enough.
How We Solve It
We start with small efficient models (3B–8B parameters: Llama 3.2, Qwen 2.5, Phi-3.5) running on modest hardware. You see real results in days, not months. Once the use-case is proven, we fine-tune on your data and scale to larger models (70B+) as your needs grow. Air-gapped or networked — your call.
What You Get
- ✓On-premise deployment (or air-gapped) on your hardware
- ✓Start small: efficient 3B–8B models running on a single GPU box
- ✓Use-case specific: RAG, classification, summarization, extraction, generation
- ✓Fine-tuning on your data when generic models aren't enough
- ✓Path to scale: bigger models, more GPUs, when ROI justifies it
- ✓Monitoring, logging, and ongoing operations support
Use Cases
Document Q&A (private)
Problem: Confidential documents can't be sent to cloud AI
Solution: Small model + on-premise RAG over your document store
Searchable, queryable AI over private data, zero cloud exposure
Patient / Clinical AI
Problem: Patient data cannot leave the hospital network
Solution: Local models for imaging triage, transcription, summarization
HIPAA-compliant AI without cloud risk
Manufacturing line inference
Problem: Need sub-second AI decisions, no internet on the factory floor
Solution: Edge AI box running optimized small models for quality / defect detection
Real-time decisions, no cloud dependency
IP-sensitive R&D
Problem: Research and engineering teams can't paste IP into ChatGPT
Solution: In-house code/text assistant on local models, fine-tuned on your codebase
Team gets AI productivity without leaking IP
⏱ 2–6 weeks for first deployment
Relevant Industries
Frequently Asked Questions
What does 'local AI' actually mean?
The AI model runs on your hardware — your servers, your data center, or an air-gapped box on your network. Your data never leaves. No API calls to OpenAI or Anthropic.
Why start with small models?
Small models (3B–8B parameters: Llama 3.2, Qwen 2.5, Phi-3.5) run on a single GPU box, give you real results in days, and let you prove the use-case before spending big on infrastructure. Most projects fail because they started with the biggest possible model.
When do we need bigger models?
Once the small-model baseline is working and you've identified specific gaps. Then we fine-tune on your data and/or move to larger models (70B+). Always grounded in measurable ROI, never speculation.
What hardware do we need?
For small models: a single GPU server (NVIDIA L4, A10, or RTX 4090 class) — typically €5K–€10K of hardware. For 70B+ models: multi-GPU setup or H100 server. We help spec the hardware based on your use-case.
Can we fine-tune on our own data?
Yes. We can fine-tune on your documents, transcripts, code, or any structured data you have. Fine-tuning runs entirely on your hardware — training data never leaves your network.
What use-cases does this actually work for?
Document Q&A (RAG over private docs), classification, summarization, extraction, transcription, code assistance, image analysis. Not yet: cutting-edge reasoning at frontier-model level — for that, you still want Claude or GPT (and accept the cloud trade-off).