Keeping data within the enterprise is the prerequisite for using AI in finance, government, healthcare, and other industries.
Keeping data within the enterprise is the prerequisite for using AI in finance, government, healthcare, and other industries. This article draws on our practical experience serving over 3,000 enterprise clients.
There are three tiers of private deployment: fully local, dedicated cloud, and hybrid architecture, chosen according to data sensitivity.
The capabilities of open-source large models can already meet most enterprise scenarios; there is no need to blindly worship closed-source large models.
First use 7B/14B small models to validate scenarios, and only after confirming value should you consider computing power investment for larger parameters.
There is no standard answer to digital transformation, but there is one iron rule: first get one scenario running and achieve results, then talk about full-scale rollout.