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Chatbot

3 min readUpdated May 2026
Chatbot
Category
Conversational AI
Interaction
Text, voice, multimodal
Types
Rule-based, retrieval, generative
Common uses
Customer service, HR, healthcare
Platforms
WhatsApp, web, Telegram, app

A chatbot (also: conversational agent, virtual assistant) is software capable of engaging in text or speech dialogue with humans. The chatbot category spans a vast spectrum — from simple FAQ bots that match keywords to rules, to sophisticated AI assistants powered by large language models that can reason, write code, and engage in extended multi-turn dialogue.

Types of Chatbots

Rule-based Chatbots

Use decision trees or pattern-matching to respond. Deterministic, predictable, and easy to audit. Suitable for well-defined workflows (FAQ, appointment booking, form filling) but brittle when users deviate from expected inputs.

Retrieval-based Chatbots

Select responses from a curated database based on input similarity. More flexible than rule-based systems, but limited to pre-authored responses. Used in early customer service deployments.

Generative Chatbots (LLM-powered)

Generate responses dynamically using large language models. Can handle open-ended questions, nuanced context, and multi-turn reasoning. Modern examples: ChatGPT, Claude, Gemini, Microsoft Copilot. Risks include hallucination and inconsistency.

Hybrid Architectures

Production systems often combine rule-based intent detection (for routing and safety guardrails) with LLM generation (for flexible response creation). Retrieval-Augmented Generation (RAG) is used to ground chatbot responses in company-specific knowledge bases.

Deployment Channels

  • Web widget — embedded chat window on websites
  • WhatsApp Business API — dominant in Malaysia for customer communication
  • Telegram bots — popular for developer communities and fintech
  • Microsoft Teams / Slack — enterprise internal use
  • Mobile app — native SDK integration
  • Voice — phone IVR, smart speakers

Key Metrics

| Metric | Description | |--------|-------------| | Containment rate | % of conversations resolved without human escalation | | CSAT | Customer satisfaction score post-conversation | | First response time | Time from user message to first bot reply | | Intent recognition accuracy | % of user intents correctly classified | | Hallucination rate | % of factually incorrect responses (LLM bots) | WhatsApp dominance — WhatsApp penetration in Malaysia exceeds 90% among smartphone users. Business chatbots overwhelmingly deploy via the WhatsApp Business API (through BSPs: Infobip, Vonage, WATI, AiSensy). This means Malaysian chatbot UX is largely conversational-text, not widget-based. Banking chatbots — All major Malaysian banks operate AI chatbots:
  • Maybank — Virtual Advisor (Maya) on MAE app; handles balance inquiries, fund transfers, loan inquiries
  • CIMB — EVA (Eva Virtual Assistant) on web and app
  • RHB — RHB Banking Chatbot on website
  • Bank Islam — BARI chatbot for Islamic banking products
Telco chatbots — Maxis (myMaxis app chatbot), Celcom (JURU), and Unifi (Uwifi) all deploy AI chatbots for billing, technical support, and plan recommendations. Government chatbots — LHDN (Inland Revenue Board) operates a tax inquiry chatbot; MySejahtera (the national health app) deployed a COVID-19 AI bot. MAMPU oversees the public sector conversational AI framework. BM support — BM chatbot quality varies significantly. Most bank chatbots offer BM but have narrower intent coverage than English. Code-switching remains a challenge; users asking mixed BM-English questions often trigger fallback paths. PDPA note — Chatbot conversations containing personal data (IC numbers, account numbers) must comply with PDPA. Logs should be retained with access controls and subject to the appropriate retention schedule. SME use cases — Malaysian SMEs are rapidly adopting WhatsApp chatbots for order management, appointment scheduling, and product inquiries. Local WhatsApp bot builders include Chatbiz, MessageBird Malaysia, and numerous no-code platforms (Landbot, Tidio, Freshdesk).
  1. Weizenbaum, J. (1966). "ELIZA — A Computer Program for the Study of Natural Language Communication Between Man and Machine." CACM 9(1), 36–45.
  2. MDEC (2023). Conversational AI Adoption in Malaysian Enterprises. Malaysia Digital Economy Corporation.