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 more than 3,000 enterprise clients.
The three-tier private deployment approach: fully local, dedicated cloud, and hybrid architecture, chosen by 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.
The biggest pitfall is underestimating ongoing costs: model iteration, knowledge base maintenance, computing power and electricity bills, and operations and maintenance are all long-term accounts.
If you are evaluating a related project, feel free to talk with us—16 years and more than 3,000 enterprise clients of experience may help you avoid some detours.