Run a private LLM fine-tuned on your data, in your own environment. We design the deployment for your data-residency and isolation needs, on your infrastructure or a dedicated setup we run for you.
One workflow to production. One platform to build on what works.
Positioning
For organizations with strict data-residency requirements, public cloud AI is not always an option.
Banking, healthcare, defense, and legal often need data to stay under their control.
We deploy and fine-tune language models that run in your environment or a dedicated setup, designed so data stays where your policy requires.
Outcomes
Private LLM fine-tuned on your proprietary data
Runs 100% locally with no internet connectivity
Deploy in your datacenter or our secure facility
Full model ownership and control
Compliance with strictest data regulations (GDPR, HIPAA, etc.)
Ongoing fine-tuning and model updates
Deployment designed around your security and data-residency needs.
We deploy and manage the LLM in your own datacenter or private cloud. Air-gapped deployment is available as a design-partner route. Your hardware, your network, your control. Suited to strict security requirements.
We host your fine-tuned model in our dedicated, isolated infrastructure in the Netherlands. No shared resources. Your model runs on dedicated hardware with no external connections.
Development and fine-tuning in our facility, production deployment in yours. We handle the complex training process, you deploy the final model on-premises for maximum security.
Approach
From data ingestion to production-ready AI in your environment
Step 01
We analyze your data, define security requirements, and design the training environment to your isolation needs, so data processing stays in your chosen environment - no cloud uploads, no external access.
Step 02
We select the right base model (Llama, Mistral, etc.) and configure hardware. We optimize for your use case - whether that's document analysis, code generation, or domain-specific tasks.
Step 03
We fine-tune the model on your data using secure, isolated infrastructure. Rigorous testing ensures accuracy and compliance. Your team reviews outputs before production deployment.
Step 04
We deploy the model to your specified environment and provide ongoing support. Continuous fine-tuning with new data, performance monitoring, and model updates - all within your secure perimeter.
Where this fits
Industries and organizations with strict data privacy requirements
Financial institutions and banks
Healthcare and pharmaceutical companies
Defense and government agencies
Legal firms handling sensitive cases
Companies in highly regulated industries
Organizations with data sovereignty requirements
FAQ
The most practical questions that usually come up before a first application actually lands in the operation.
View all questionsChatGPT and Claude are cloud services where your data is sent to external servers. With an in-house LLM, the model runs in your own environment or a dedicated isolated setup, designed so no data is sent to external services. Your data stays under your control.
For on-premises deployment, you'll need NVIDIA GPUs (A100, H100, or similar). Requirements depend on model size and performance needs. We can also host everything in our facility, eliminating your hardware investment.
For general knowledge, cloud models have an edge due to massive training data. But for your specific domain - your documents, your processes, your terminology - a fine-tuned in-house model will outperform because it's trained exclusively on your data.
All fine-tuning runs in your environment or a dedicated isolated environment we set up for the engagement. We bring the training infrastructure to your data, not the other way around. No cloud uploads. No external API calls.
First step
Let's discuss how an in-house LLM can give you the power of AI without compromising on security.
Response time
We typically respond within 24 hours
