Rule-based vs AI-powered chatbots
Traditional chatbots follow decision trees — if the customer says X, reply with Y. These are predictable but brittle. They fail the moment a question falls outside the script, and maintaining the decision tree becomes painful as it grows.
AI-powered chatbots use language models to understand intent, not just keywords. They handle variations in phrasing, follow-up questions, and multi-topic conversations naturally. Combined with RAG, they can answer questions grounded in your actual documentation and policies.
When chatbots make sense for SMBs
If your team answers the same questions repeatedly — pricing, availability, service areas, booking processes — a chatbot can handle the majority of these interactions 24/7. This frees staff for complex queries that genuinely need a human.
Chatbots also work well for lead qualification. They can gather key details (budget, timeline, requirements) before routing qualified leads to your team, reducing time spent on poor-fit enquiries.
Getting chatbots right
The biggest mistake is deploying a chatbot that hallucinates or gives incorrect answers. Build guardrails: limit the chatbot's scope to topics you've verified, include fallback-to-human options, and monitor conversations regularly. A chatbot that confidently gives wrong answers is worse than no chatbot at all.
We build custom chatbots trained on your actual business data with clear boundaries and escalation paths.