In brief
Local AI is AI that is not entirely dependent on an external cloud environment. Processing takes place locally, on the organisation’s own hardware or within its own network. This keeps the infrastructure closer to the organisation using the AI.
Why is this relevant?
Much AI usage takes place via external platforms involving subscriptions, API charges and third-party rules. Local AI reduces that dependency. This is particularly beneficial when internal expertise, privacy, continuity or predictable costs are important.
Is local AI the same as edge AI?
The terms overlap significantly. Edge AI emphasises that processing takes place close to the data source. Local AI emphasises that the AI is available within your own environment. In practice, the terms are often used interchangeably.
What can you do locally?
This includes answering internal queries, searching through documents, using knowledge bases, creating summaries, running agents or supporting workflows. Not everything needs to be hosted locally, but components involving sensitive data or requiring high availability can often benefit greatly from it.
What should you bear in mind?
Local AI requires sound decisions regarding hardware, models, access, updates and management. It is not a one-off solution, but an infrastructure choice. The best solution combines local capabilities with clear agreements on what, if anything, may still be outsourced.
From concept to practice
How AITJE applies this
AITJE Assistent You can use local models on your own device or server. Your team will be provided with a dedicated chat environment featuring a knowledge base, accounts and roles. We assess the required capacity and available hardware and configure the software accordingly. The local core does not require any external AI calls; any web searches, external models and other online tools have separate dependencies.
Visit AITJE AssistentFrom explanation to application
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