CSPs can deploy genAI to create scalable, cost-efficient solutions that meet industry-specific needs. Large language models (LLMs) have proven themselves to be powerful, but also expensive, generic, and often unsuited to edge environments. Small language models (SLMs) offer a compelling alternative—delivering privacy-preserving, low-latency intelligence tailored to CSP and enterprise use cases. Building on the foundations of earlier phases, this Catalyst introduces a genAI-powered framework that helps CSPs select, deploy, and manage SLMs across their network and edge ecosystems. The solution uses a structured methodology for evaluating SLM suitability. This is based on use case requirements, such as latency tolerance, privacy constraints, model accuracy, and compute availability. This ensures enterprises can select and deploy the right SLM for each business scenario, without trial-and-error experimentation. Crucially, it contains an edge-enabled genAI framework which integrates SLMs into CSP edge environments and exposes them via unified APIs for enterprise access. The solution also supports federated learning, allowing enterprises to fine-tune models locally without transferring sensitive data. Use cases include AI-powered network optimization, real-time IoT insights, predictive maintenance, and hyper-personalized customer support—all delivered at the edge. To simplify adoption, the solution caters for non-technical users to train, fine-tune, and deploy models through a low-code interface. This brings genAI within reach of business teams, not just data scientists. This makes technical engineering far more accessible while preserving performance, compliance, and control at the edge. The Catalyst will track impact through faster training times, successful edge deployments, and increased API uptake. It will also measure gains in energy efficiency and real-world business outcomes. By turning SLMs into scalable, monetizable tools, this Catalyst helps CSPs unlock new B2B2X growth—where intelligence meets impact at the edge, and innovation moves at the speed of demand.