Is ChatGPT GDPR compliant for business use?
The consumer version of ChatGPT is generally not GDPR compliant, because conversations may be retained and used for training with no Data Processing Agreement or EU data residency guarantee. A self-hosted or private LLM avoids this by keeping every prompt and document inside infrastructure you control, so no personal data leaves EU jurisdiction. We build the GDPR-compliant alternative around open models and open standards, not a single vendor.
What is private AI?
Private AI means running large language models, RAG pipelines and AI agents on infrastructure you control, on-premise or in a dedicated EU environment, rather than sending data to external cloud APIs. Your prompts, documents and model weights never leave your perimeter and are never used to train someone else's model, giving you full data sovereignty and alignment with GDPR and the EU AI Act by design.
Which open-source models and tools can run on-premise?
Capable open-weight models such as Llama, Mistral, Mixtral, Qwen and DeepSeek run well on your own GPU servers, with smaller models on a single 24GB GPU and 70B-class models on multi-GPU setups. We serve them through whichever inference engine fits, for example vLLM, Ollama, llama.cpp, SGLang, LocalAI or Hugging Face TGI, all exposing the OpenAI-compatible API so you are never locked in. We help you select, fine-tune and deploy the right combination for your accuracy, latency and budget.
How do you stop sensitive data and PII from leaking into an LLM?
We add a PII protection layer, typically built on open tooling like Microsoft Presidio, that detects and redacts names, emails, financial and health data before prompts reach the model, with optional reversible tokenisation so responses stay personalised. Combined with on-premise hosting and a local RAG and vector store, no sensitive information ever leaves your network.
Is self-hosting an LLM cheaper than using cloud APIs?
It depends on usage. For low or sporadic volume, cloud APIs are cheaper; for sustained, high-volume workloads, on-premise typically wins on total cost over a two- to three-year horizon, and the data-sovereignty benefit is structural rather than a line item. We size the hardware and architecture to your actual usage so the break-even works in your favour.