Private LLM for business: on-premise AI without sharing your data

The problem: using AI without handing over your data

Every call to a model behind a public API sends the text of the request to a third party. For many tasks that is fine; for others it is not. Customer personal data, patient information, bid documents, or a company's internal figures often come with usage rules, confidentiality agreements, or simply an internal policy that rules it out.

A private LLM changes the risk model: the model runs on infrastructure you control and the data is processed there. You decide what is logged, who has access, and how long anything is kept.

Who on-premise AI is for

It usually fits companies that match one or more of these situations. For example, a firm that prepares proposals for public tenders could use a private LLM to summarize tender documents and draft responses without uploading its clients' files to an outside service. That is an illustration, not a client case.

• They handle personal or sensitive data from customers, employees, or patients.
• They have confidentiality commitments with third parties or strategic information that must stay inside.
• They operate in sectors with audit requirements, such as financial services, healthcare, or the public sector.
• They already spend heavily on AI API calls and want to know whether their own infrastructure would pay off.

Open-weight models versus commercial APIs

Commercial APIs give you the largest models without operating anything, and for many cases they are the best option. Open-weight models can be downloaded, adapted, and run on your own infrastructure. They are smaller, but well chosen and, when needed, fine-tuned on your data, they handle specific tasks very well: summarizing, classifying, extracting, or answering questions over your documents.

The cost structure changes too. With an API you pay for every call and spending grows with volume. With your own infrastructure you pay for servers or GPUs, and the cost per request falls as usage rises. Which one wins depends on your volume, your latency needs, and your team; we work it out with your numbers, not generic averages.

Where it runs: private cloud, on-premise, or edge

There is no single way to run private AI. We choose with you between:

• Private cloud: the model runs in your own cloud account, inside your network and under your access controls.
• On-premise: the model runs on servers you own, in your data center or your office.
• Edge: small models that run on the device or on site, useful when connectivity is poor or the answer must be immediate.

How we work

A private deployment is measured against the API alternative before it reaches production:

1. We map the data, the rules that govern it, and the tasks the AI must solve.
2. We choose the open-weight model and the deployment target, and estimate the infrastructure needed.
3. We set up a pilot in your environment and compare it with the API alternative on your cases.
4. We configure access, logging, and monitoring, and connect it to your systems.
5. We move to production with documentation and a plan for updating the model.

What we deliver and the technology behind it

We deliver the model deployed on your infrastructure, an API your applications can call, search over your documents (RAG) when the task needs it, access controls and logs, and the documentation to operate it. We work with open-weight models, inference servers such as vLLM, Ollama, or llama.cpp, containers, and the tools of your cloud. A private model can also power AI agents that never send conversations outside.

On regulation: in Colombia, Law 1581 of 2012 governs the processing of personal data. Keeping data inside your infrastructure can make it easier to meet your obligations, but it does not replace an analysis of your case. This is not legal advice; review your obligations with your legal team or a specialized lawyer.