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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 challenges around an on-premise LLM
The value of an on-premise LLM depends on a design that serves operations, not only a demonstration.
Solutions that do not fit
When privacy and per-token cost push you to run AI locally, a template tool forces the process to bend to the product. The result is generic answers and manual work that does not disappear.
Fragmented context
Without ordered access to your data and systems, the solution responds incompletely. Every decision depends on information scattered across tools and emails.
No control or measurement
A project with no owners, limits or metrics soon stalls. We leave traceability and clear criteria so every result is assessed with evidence.
APPROACH
From need to running models on your hardware with full control
We sequence technical and operational decisions so the first release is useful and maintainable.
Context map
We document data, systems and use cases to define how an on-premise LLM must fit daily work, before proposing architecture.
Data and security
We define access, sensitive data and controls aligned with ENS practice when required. Privacy is designed from the first sprint.
Incremental releases
We prioritise a usable, measurable use case; the rest is planned against value. Every release leaves something running for running models on your hardware with full control.
Handover and ownership
Code, data and documentation stay under your company's control. Your team can maintain and evolve the solution without depending on us.
DELIVERABLES
What an on-premise LLM leaves ready
We deliver an operational foundation your team can govern, measure and continue to evolve.
Working solution
an on-premise LLM ready for the agreed journeys and reviewed before wider adoption.
Documented integrations
Connections, systems of record, permissions and limits recorded so the solution can be maintained without relying on individual memory.
Code and configuration
Repositories, workflows and configuration under your company's control, transferable to another team when needed.
Evolution plan
Metrics, accountable owners and a prioritised backlog to decide what improves after the first release.
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.
