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ON-PREMISE AI
On-premise LLM: your AI without moving your data
We run language models on your own infrastructure to cut token cost and keep data inside your organisation, with the right hardware sizing and inference stack.
On-premise LLM: your AI without moving your data
An on-premise LLM matters when you need to run language models on your own hardware for security or cost, not a third-party cloud solution. Before selecting tools, we define the journeys, data and decisions that must remain under your company's control.
We work from Barcelona and Madrid and remotely with teams across Spain, Europe, LATAM and the United States. The process combines decision sessions, verifiable releases and documentation so progress stays visible.
We hand over code, repositories, configurations and documentation in accounts owned by your company. You can therefore audit, evolve or change supplier without relying on private access or licences.
We run language models on your own infrastructure to cut token cost and keep data inside your organisation, with the right hardware sizing and inference stack. That is why we prioritise a maintainable, measurable solution ready to evolve without rebuilding its foundations.
THE CHALLENGE
The challenge of an on-premise LLM
privacy and per-token cost push you to run AI locally
Data confidentiality
Sending sensitive information to cloud models breaches internal policies or industry regulations.
Infrastructure costs hard to predict
Running your own LLM requires sizing GPU, memory and storage without overpaying.
Model governance
An internal model must control what it answers, to whom and with which data, in an auditable way.
APPROACH
How we fix running models on your hardware with full control
We move forward with measurable decisions and releases that already add value.
Assessing requirements and data
We define which use cases, volume and confidentiality level the local model must support.
Designing the infrastructure
We size hardware and software to serve the model with acceptable latency and cost.
Deploying and tuning the model
We install and fine-tune the LLM on your data, with human review of responses.
Governance and maintenance
We set access rules, version updates and continuous monitoring.
DELIVERABLES
What an on-premise LLM leaves ready
We deliver an operational foundation your team can govern and keep improving.
Operational on-premise LLM
Model deployed on your infrastructure and available to authorized users.
Right-sized infrastructure
Hardware and software configuration documented and tuned to real load.
Access and usage policies
Permission rules, allowed data and exceptions defined and traceable.
Update and monitoring plan
Guidelines to keep the model current and control its performance and security.
TRUST
Technology applied to real operations
We size GPUs (NVIDIA/AMD) and operate the full loop: model, inference and integration with real processes.
- Scope, assumptions and risks visible before the project is committed.
- Integrations, permissions and data designed for real operations.
- Reviewable releases before the solution reaches the whole organisation.
- Code, configuration and documentation under your company's control.
FAQ
Questions about on-premise LLM for business
Have a project in mind?
Tell us what you need around on-premise LLM for business. We will help you turn it into a clear, viable delivery plan.
