This topic covers general-purpose large language model (LLM) chatbots and the conversational platforms built around them for customer service, conversation intelligence, personal assistants, and CX automation. These systems combine foundation models (ChatGPT, Google Gemini, Anthropic Claude, Mistral) with tooling for retrieval-augmented generation, fine-tuning, embeddings, and governance to deliver real-time text and voice interactions across channels. Relevance: enterprises are moving from experimental pilots to production deployments that require multimodal inputs, strong data controls, and operational tooling. Key trends include integration of LLMs into workflow apps (Microsoft 365 Copilot), cloud ML platforms for model lifecycle (Google Vertex AI), private/customizable model hosting (Cohere, Mistral), and domain-grounded answer engines (Perplexity). Conversation-intelligence vendors (Observe.AI, Skit.ai) add voice agents, live agent assist, and automated QA, while no-code platforms (Duckie) enable rapid creation of support agents that act on knowledge bases and take actions across systems. What to evaluate: model capabilities (multimodality, latency, grounding), data privacy and governance, integration with existing contact-center and CRM systems, support for voice and omnichannel flows, developer APIs and enterprise deployment options, and no-code vs. custom engineering trade-offs. By combining foundation models with retrieval, monitoring, and compliance layers, these platforms aim to shift conversational AI from isolated prototypes to scalable, auditable services that improve customer experience and agent productivity without excessive risk.