Glossary
Conversational AI
Conversational AI is technology that enables software to understand, process, and respond to human language in a way that feels like a natural dialogue, rather than a rigid command-and-response interaction.
General definition
Conversational AI combines large language models (LLMs), natural language processing (NLP), and dialogue management to let computers interpret intent, maintain context across turns, and generate relevant replies. Unlike scripted chatbots that follow decision trees, conversational AI systems can handle open-ended questions and recover gracefully when a user goes off-script.
- Intent recognition - understanding what the user wants, not just the exact words used
- Context tracking - remembering earlier turns in a conversation to give coherent follow-up responses
- Entity extraction - pulling structured data (dates, names, amounts) from free-form text
- Response generation - producing fluent, contextually appropriate replies via an LLM or retrieval system
Common deployments include customer support chatbots, in-app help assistants, voice interfaces, and AI agents embedded in business workflows. The quality of a conversational AI system depends on the underlying model, the quality of grounding data (see RAG), and how well the dialogue is designed.
In the Ethora ecosystem
The Ethora AI SDK is built for teams that want to embed conversational AI directly inside their own product. You can deploy a RAG bot grounded on your own knowledge base, run an LLM agent that participates in group chat rooms, or add an embeddable AI chat widget to any web page, all without building the messaging infrastructure yourself.
For regulated industries such as healthcare or finance, Ethora’s self-hosted LLM agent option keeps every conversation on your own infrastructure, so sensitive data never leaves your environment. That matters when compliance frameworks require data residency or audit trails over every AI interaction.
