As enterprises develop advanced applications powered by Large Language Models (LLMs), common concerns are emerging. Many large organizations recognize the value of LLMs trained on public data and are now envisioning the potential of models trained on their proprietary data. However, for enterprises that treat data as a critical asset, the idea of sending it outside their network — regardless of the assurances provided by cloud providers — remains a significant challenge. Additionally, while public LLMs continue to improve, complex and enterprise-specific problems often require models that are better tailored to the organization’s unique business domain.
We have a unique approach for these scenarios. Persistent’s Fine-tuning Lab is a flexible, customizable environment that enables enterprises to transition from external LLMs to locally hosted models. This lab provides Fine-tuning pipelines that integrate seamlessly with enterprise data, ensuring greater control, security, and domain-specific optimization. To learn more, download our new whitepaper today.
