Why your business intelligence belongs on a local LLM

Language models are now good enough to answer questions about your own operation. The question most businesses skip is where that data goes when you ask.

For a retail group the answer is a commercial risk. For a hospital or a law practice it can be a regulatory one.

What leaves the building

When staff paste a sales report, a contract or a patient summary into a hosted AI tool, that data travels to a third party's servers. Depending on the provider and the plan, it may be retained, logged or reviewed.

Most organisations never decided this on purpose. It happened one browser tab at a time.

What a local model changes

A locally hosted model runs on hardware you control, in your office or in a private cloud account in your name. Questions and answers stay inside that boundary. The model can be connected to your ERP, hospital system or document store and answer from your data without sending it anywhere else.

Open-weight models have closed much of the gap with hosted ones for the work operations teams actually need: summarising, drafting, searching across documents and answering questions about structured data.

When local is the right call

• Regulated data: patient records, legal case files, and personal data covered by the GDPR or India's DPDP Act.
• Commercially sensitive data: pricing, margins, supplier terms and anything a competitor would pay to read.
• High, predictable volume, where per-request fees on a hosted service add up month after month.

When it is not

A local model is the wrong answer when the data is not sensitive, the volume is low, or the task needs the strongest reasoning available. Running your own infrastructure has a real cost in hardware and upkeep. For a small team summarising public information, a hosted tool with a business-grade data agreement is cheaper and good enough, and we will say so.

How we deploy it

We start with the data boundary rather than the model: what the system may read, who may ask it questions, and what gets logged. Then we choose a model sized to the hardware and the task, connect it to the systems it needs, and test its answers against questions your team already knows the answers to.