This topic compares leading conversational large language models (LLMs) and chatbot systems—ChatGPT, Google Gemini 3, and Anthropic Claude—through the lens of customer service chatbots and conversation intelligence tools. It explains how modern conversational AI combines text LLMs, voice agents, TTS/transcription, and audio-quality tooling to support 24/7 support, call automation, and post‑call analytics. Relevance (2026-01-19): enterprises are deploying multimodal LLMs and voice-first agents at scale while facing tighter privacy, compliance (e.g., HIPAA), and accessibility requirements. Advances in real‑time transcription, expressive TTS, and noise suppression have changed integration patterns: LLMs now power agent augmentation, automated booking, and conversation summarization across channels. Key tools and roles: Olark provides accessible, multi‑channel web chat and AI chatbots with a rich JavaScript API for integrations and agent augmentation; ElevenLabs supplies production-grade TTS, voice cloning, and transcription for high‑quality voice agents; OpenCall AI focuses on HIPAA‑compliant phone and messaging automation for patient booking and sales workflows; Simple Phones offers configurable AI phone agents that integrate with CRMs and webhooks; Krisp improves audio quality, real‑time transcription, and meeting notes to feed conversation‑intelligence pipelines. How to evaluate: prioritize multimodal understanding, latency, deployment and privacy options (hosted vs. private), safety/steerability, and integration/APIs. Broad ecosystems (ChatGPT) often offer plugins and developer tooling; Gemini 3 emphasizes multimodal reasoning and search/context integration; Claude emphasizes safety, control, and behavior shaping. Practical implementations commonly pair an LLM for dialogue with TTS/transcription and audio tooling to meet accessibility, compliance, and operational needs.