Question 1

What is an AI chatbot and how is it different from a rule-based bot?

Accepted Answer

A traditional rule-based chatbot follows a fixed decision tree and breaks when users phrase things unexpectedly. An AI chatbot is powered by a large language model (LLM) — it understands natural language, handles follow-up questions, remembers context within a conversation, and can be grounded in your knowledge base to give accurate, human-like answers. The result is far higher resolution rates and a much better user experience.

Question 2

Can the chatbot answer questions using our own documents and data?

Accepted Answer

Yes. We connect the chatbot to your knowledge base — help docs, product catalogues, FAQs, policies, and databases — using Retrieval-Augmented Generation (RAG). The bot retrieves the most relevant information before answering, so responses are grounded in your actual content rather than generic or made-up answers.

Question 3

Which channels can the chatbot be deployed to?

Accepted Answer

We deploy AI chatbots to your website (embeddable widget), WhatsApp Business API, Facebook Messenger, Instagram, Slack, Microsoft Teams, and mobile apps. The same underlying AI brain serves every channel, so the experience stays consistent everywhere your customers are.

Question 4

Can the chatbot hand off to a human agent when needed?

Accepted Answer

Absolutely. We build smart escalation: when the bot detects frustration, a complex issue, or an explicit request for a human, it seamlessly hands the conversation — with full context — to your support team via your existing helpdesk (Zendesk, Freshdesk, Intercom, or a custom dashboard).

Question 5

How much does it cost to build an AI chatbot?

Accepted Answer

A website support chatbot grounded in your docs typically starts from $5,000–12,000 for the build, plus usage-based LLM costs (often $50–500/month depending on volume). Multi-channel bots with CRM integration, analytics, and human handoff range higher. We provide a fixed quote after scoping your channels and integrations.

Question 6

How do you measure and improve chatbot performance?

Accepted Answer

We build an analytics dashboard tracking resolution rate, deflection rate, escalation rate, user satisfaction (CSAT), and unanswered questions. Unanswered questions feed back into the knowledge base so the bot continuously improves. We review performance with you and tune prompts and retrieval regularly.

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