Local artificial intelligence can mean the model or the inference process running on the organisation’s own infrastructure. Its value should be judged by data flow and operational need, not by a single sentence like “it never touches the internet”.
When should it be considered?
Not sending sensitive documents to an external service, low latency, predictable cost, using special hardware, or the need to integrate with internal networks are strong candidates for local inference.
The data pipeline matters as much as the model
Where documents live, which users can query them, what gets written to logs, and in which system the model output is used must be designed upfront. Running the model inside alone does not provide data security.
Starting architecture
- Data classification and access layer.
- Lifecycle for the embedding or search store.
- Model serving and resource consumption limits.
- Outputs that require human approval.
- Quality, latency and cost measurements.
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