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Home/Glossary/IVR (Interactive Voice Response)

IVR (Interactive Voice Response)

An automated phone system that routes callers through menus using voice or keypad input. Traditional IVR uses rigid decision trees; AI voice agents replace or augment IVR with natural-language conversations that qualify callers and book appointments without menu trees.

Written by Max Zeshut

Founder at Agentmelt · Last updated Oct 26, 2026

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TTS (Text-to-Speech)

Technology that converts written text into spoken audio. AI voice agents use TTS to speak naturally on phone calls. Modern TTS models (e.g. ElevenLabs, OpenAI) produce near-human speech quality, enabling voice agents that sound conversational rather than robotic.

Intent Classification

Identifying the purpose behind a user's message (e.g., 'I want a refund' → refund intent). AI agents use intent classification to route requests to the right workflow: support tickets, sales inquiries, billing questions, or escalation to a human. Modern LLM-based classifiers handle nuance and multi-intent messages.

Sentiment Analysis

The process of identifying emotional tone (positive, negative, neutral) in text or speech. AI agents use sentiment analysis to prioritize angry support tickets, gauge prospect interest in sales conversations, monitor brand perception on social media, and trigger human escalation when frustration is detected.

Latency

The time delay between sending a request to an AI model and receiving a response. Low latency is critical for real-time applications like voice agents (where delays feel unnatural) and live chat support. Factors include model size, infrastructure, and whether the agent needs to call external tools before responding.

Conversational AI

AI systems that engage in natural-language dialogue with humans—understanding intent, maintaining context across turns, and generating relevant responses. Conversational AI powers chatbots, voice assistants, and support agents. Modern implementations use LLMs for open-ended understanding rather than rigid intent-matching rules.

Multimodal AI

AI systems that process and generate multiple types of data—text, images, audio, video, and code—within a single model or agent. Multimodal agents can analyze a screenshot, describe it in text, generate a response audio file, or review a video for content moderation. This capability is critical for design, moderation, healthcare, and voice agents.

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