Open-weight models are rapidly closing the gap with closed models. That is why we have chosen to specialise in self-hosted LLM solutions for organisations that cannot or do not want to send business-critical data outside their own environment.
Data sovereignty
Data, prompts and outputs stay within your own environment, on-premises or in your own cloud tenant.
Predictable costs
Capacity you control, rather than token-based pricing that rises with usage.
No lock-in
Switch when a better open-weight model becomes available, without rebuilding your platform.
From GPU to AI agent, in your own environment
- Architecture and sizing for GPU and inference platforms
- Model selection, evaluation and benchmarking
- Deployment, operation and scaling of open-weight models
- Fine-tuning on domain-specific data
- Security, access control, logging and monitoring
- Integration with AI agents, RAG and existing systems
Access to data is not the same as learning
The model looks things up
Retrieval-Augmented Generation lets a model search a document collection and incorporate relevant passages before responding. The model itself does not change. It has access to the information, but has not learned your patterns.
The model learns
Carefully prepared examples are used to adjust the model's weights. The aim is to teach it your specialised behaviour, terminology, decision-making patterns and output structures.
Often, it is the combination that delivers value: fine-tuning for the way you work, RAG for the latest knowledge.
Should your AI stay in-house
We are happy to start with a conversation about your data, requirements and use cases, and whether open-weight models are the right choice for you.
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