Large language model (LLM)
A neural network trained on large volumes of text to predict the next token, giving it the ability to understand and generate language across a wide range of tasks.
In more detail
A large language model is trained to predict the next token in a sequence. Scaled up across enough data and parameters, that single objective produces a system that can summarize, classify, extract, translate, draft, and reason well enough to be useful across most language tasks without task-specific training.
In a business automation context, the LLM is not the whole system. It is the component that reads unstructured input and produces a decision or a piece of language. The reliability, the data access, and the actions come from the software wrapped around it: the connectors, the guardrails, and the workflow engine.
Where this shows up at Ceven
Ceven uses language models as the reasoning steps inside a workflow, never as the whole product. The model decides and drafts, connectors read and write across the customer's 1,000+ connected tools, human-approval gates catch the consequential steps, and the audit trail records what happened. The model is one part; the orchestration around it is what makes it dependable.