What local AI means
Local AI means that a model runs on a computer or server controlled by the organisation. Documents and prompts therefore do not necessarily leave the organisation. This matters for internal reports, technical documentation, personal data and trade secrets.
Privacy is not automatic
A locally installed model is not secure by default. Access rights, encryption, logging, backups, updates and sensitive-data handling must still be designed. It is equally important to stop users from copying unchecked outputs into critical processes.
Hardware and responsiveness
Larger models require substantial memory and capable GPUs. Smaller models are faster and cheaper but have narrower capabilities. A practical architecture often combines a local model for routine tasks with a controlled external model for harder cases.
RAG and organisational knowledge
A model does not need to be retrained on all company knowledge. In a retrieval-augmented generation workflow, the system finds relevant passages in a verified document collection and adds them to the prompt. Answers become more traceable and content can be updated without retraining.
Measuring quality
Before deployment, prepare realistic test cases, expected answers and failure criteria. Measure not only accuracy but also latency, cost, rejection rate and frequency of human correction. AI is valuable when it improves the whole process—not merely when it creates an impressive demo.