A large language model is a machine learning model that uses a huge amount of data to perform natural language processing tasks. LLMs can generate, translate, summarise, and predict text. They can also answer questions and perform other tasks. LLMs can automate tasks like content creation, personalise ads, and analyse customer data.
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A subset of AI that involves algorithms allowing machines to learn patterns from data and make decisions without explicit programming.
A subset of AI that involves algorithms allowing machines to learn patterns from data and make decisions without explicit programming.
A standard introduced by Anthropic that allows AI systems (like large language models and agents) to securely connect to external tools, data sources, and applications in a consistent way.
Instead of building custom integrations for every system (e.g. CRM, databases, ad platforms), MCP provides a shared “plug-in style” interface so models can access data (documents, APIs, databases), use tools (search, analytics, workflows) and maintain context across systems in a structured way.
Systems in which multiple AI agents work together, through collaboration, coordination, or division of labour to complete tasks that would be difficult for a single agent to handle alone.
A media agency deploys a multi-agent system where separate AI agents handle planning, creative generation, and optimisation, working together to run a campaign.
A type of AI that can work with and generate different types of content, such as text, images, audio, and video. Unlike traditional AI that handles just one type of data, it can combine multiple formats to create a more complete understanding and output.
A brand uses multimodal AI to create a campaign from a single brief, producing ad copy, visuals, and video content tailored to different audiences. This helps advertisers create more consistent and personalised campaigns across different formats and channels, more efficiently.
The system layer that coordinates different AI models, tools, and workflows so they work together to complete a task. It decides which tools to use, in what order, and how information is shared between them. It's also sometimes called an Agent Orchestration Platform (AOP).
A marketing platform uses an orchestration layer to combine audience data, generate AI-powered creative, and automatically launch and optimise campaigns across different channels.
Orchestration layers help automate complex workflows, making it easier for multiple AI tools to work together as one system.
The process of tailoring ads to individual users based on their behaviour, preferences, and demographic information is often powered by machine learning and AI.
A type of artificial intelligence that uses historical data, statistics, and machine learning to predict future outcomes, behaviours, or trends. Instead of creating new content, it looks at patterns in existing data to estimate what is likely to happen next.
A marketing platform uses predictive AI to forecast which audiences are most likely to convert, helping advertisers focus their spend and improve campaign performance. It supports better decision-making by improving targeting, reducing wasted spend, and increasing overall effectiveness.