Term explanation
Agentic AI refers to artificial intelligence systems that act autonomously to achieve multi-step goals — without requiring a human to trigger each action individually. Unlike traditional AI that responds to single prompts, agentic AI plans, decides, and executes sequences of tasks on its own, often integrating with external tools and data sources. In enterprise settings, agentic AI is increasingly used to automate complex workflows such as reporting, content creation, and communication. In the domain of presentations, this approach is realised through the Large Presentation Model (LPM) — an agentic AI system that orchestrates the entire presentation cycle in an enterprise context.
LIZ AI is built on agentic principles: it orchestrates the entire presentation cycle — from pulling live data to updating slides and enforcing brand compliance — without requiring manual intervention at each step.
Learn more →A mix between in-person and virtual participants for an event or a lecture is called a hybrid audience. Working with a hybrid audience may be challenging, as it requires the presenter to find ways to engage both the live and the virtual audience.
Learn moreInternal communication is particularly important for corporate communication. It communicates important information from leadership to staff so that they can do their jobs in the best possible way and work processes run well.
Learn moreA .potx file is a file which contains, styles, texts, layouts and formatting of a PowerPoint (.ppt) file. It's like a template and useful if you want to have more than one presentation with the same formatting.
Learn moreAI grounding is the process of anchoring an AI system's outputs to verified, real-world data rather than relying solely on knowledge encoded during model training. A grounded AI retrieves relevant, up-to-date information from external sources before generating a response. This significantly reduces the risk of AI hallucinations and ensures that outputs are accurate, current, and contextually relevant — a critical requirement for enterprise AI applications where factual reliability is non-negotiable. Grounding is a core technique used in LLM-powered systems.
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