Large language models (LLMs) are enabling development of highly capable generative AI applications. But these generic models can be expensive and energy-intensive to run, prompting growing interest in bespoke smaller language models (SLMs) that promise greater cost-efficiency, deployment flexibility and enhanced privacy control. While fine-tuning LLMs on smaller datasets for specific use cases is a prolonged and resource-intensive process, fine-tuning pre-trained SLMs with domain-specific data can be accomplished swiftly. For example, an insurance company could fine-tune a pre-trained SLM with its policy documents in just two to three hours. Using an SLM allows for the implementation of a generative AI model on devices with relatively low processing and memory requirements, reducing overall cost of ownership by around 30%. As they can draw on customer, network, operations and billing data, CSPs could build SLMs both for internal use and for enterprise customers, opening up new revenue streams. This Catalyst plans to introduce an architectural framework wherein all CSP data is securely centralized on a single platform, facilitating the creation of clean and pre-processed datasets. This end-to-end framework would empower CSPs to extend this service to other enterprises, which could use their proprietary data to efficiently and effectively create their own generative AI models. CSPs could expose pre-trained SLMs arising from the framework as APIs so that enterprises can access and use them seamlessly, without needing a team of technical experts. Until now, implementing generative AI has required specialized skills in machine learning, data science and AI development. Enterprises may struggle with a shortage of talent or expertise in these areas, making it challenging to develop and deploy AI solutions, while also addressing ethical concerns and regulatory requirements. Once complete, this Catalyst project will help CSPs and businesses overcome these challenges by enabling them to harness pre-trained, domain-specific models that perform better than generic LLMs, while offering lower latency and reduced power consumption.