Screendragon has launched AI Hub, a new capability within its Agentic Marketing Orchestration platform that enables enterprise marketing teams and agencies to build, deploy, and govern their own AI agents directly inside live workflows. The feature is designed to move teams beyond AI experimentation and into real execution, without requiring machine learning or data science expertise.
What AI Hub does
AI Hub provides a visual interface for building AI agents that plug directly into existing marketing workflows. Teams can automate tasks such as briefing, content creation, approvals, and compliance checks — all within the same system they already use for managing work. The agents can be configured to keep outputs consistent, compliant, and on-brand, and teams can control which AI models are used and when.
AI Hub is part of a broader AI offering across the Screendragon platform. The full stack includes:
- Embedded AI Agents – Pre-built agents that automate common tasks inside workflows
- AI Hub – A flexible environment to build and manage custom agents
- AI Studio – Advanced tools for designing and optimizing AI agents
- AI Foundry – Expert support to build and scale bespoke AI-driven workflows
This gives teams a progression path: start with out-of-the-box agents, then evolve toward fully customized enterprise-grade AI execution.
Cost and model control
AI Hub addresses a common pain point: AI usage grows fast, and costs can grow faster. The platform lets teams route work across different AI models based on cost, speed, and performance. It also supports open-source models where appropriate, and avoids lock-in to a single AI provider.
Availability
AI Hub is available immediately to all Screendragon customers. The company is based in Cork, Ireland.
Bottom line
AI Hub is a practical step for marketing teams that have been experimenting with AI but haven't integrated it into their core workflows. By putting agent creation into a visual, no-code interface and tying it directly to live processes, Screendragon aims to close the gap between AI experimentation and production use. The ability to control model selection and cost adds a layer of governance that enterprise teams typically require before scaling AI across the organization.