Chatbot

Your customer writes at 11 PM: "Where is my delivery?" Three seconds later, they have the tracking number. Nobody was sitting at a desk. A chatbot answered — software that simulates conversations, via text or voice.

Three generations, three worlds

The first generation follows if-then logic. "Order status" → tracking number. Works as long as the customer hits the exact expected phrasing. They do not? Dead end.

Generation two understands intent. NLP and machine learning recognize that "where's my package?" means the same as "order status." Requires training data. Gets better over time.

Generation three runs on large language models. GPT, Claude, or Llama hold free-form conversations, retain context across multiple messages, and formulate individual responses. The catch: without a connection to real data, the model occasionally invents facts.

The math behind customer support

A chatbot interaction costs an average of $0.50. A human agent: $6.00. Gartner projects that conversational AI will save roughly $80 billion in contact center costs by 2026.

That does not mean: replace all agents. It means: automate standard requests so the team has time for the cases that actually need a human. The starting point? A clearly scoped use case — appointment booking or order inquiry. After that, a RAG-enabled company chatbot with access to your internal systems.