Modern AI chatbots aren't rule-based — they're LLM-based
Legacy rule-based chatbots ('if user types X, reply Y') become useless outside their narrow set. Modern chatbots run on LLMs (Claude, GPT, Gemini); natural-language understanding and response.
RAG (Retrieval-Augmented Generation) integrates company-specific knowledge. FAQ docs, product catalog, manuals — read by the chatbot, with the right answer synthesized.
Escalation and human handoff define the quality
A well-designed chatbot knows its limits. Complex topics, frustration signals, critical decisions — auto-escalate to a human agent. With conversation history.
The quality of the handoff drives customer satisfaction. A bad handoff means 'start over'; a good one carries the full context to the agent. We make this standard.
ROI: 50–70% drop in support team load
For most enterprise customers, AI chatbot lifts 50–70% of support team load. Repeating questions (price, location, product info) → chatbot; complex → human.
Cost: monthly LLM API ($100–$1,000 for 10K–100K messages); prompt caching cuts 50%+ more. Typical ROI is clear within 3–6 months.
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