Chatbot Basics
Msghub's AI chatbot is knowledge-base grounded, meaning it answers questions from your data—not hallucinations. It works across all 6 channels (SMS, WhatsApp, Email, RCS, Instagram, Web Chat) and resolves 80% of customer queries without human intervention.
How the Chatbot Works
1. Customer Asks a Question
A customer messages you on WhatsApp, web chat, or any channel:
Customer: "Where's my order?"
2. Chatbot Receives the Message
Msghub receives the message and routes it to the chatbot.
3. Chatbot Searches Knowledge Base
The chatbot searches your knowledge base for relevant information:
- FAQs
- Product documentation
- Policies
- Order data (if integrated)
4. Chatbot Responds
The chatbot generates a response based on your knowledge base:
Chatbot: "I found your order! Here's the latest: Order #7842, Status: Out for Delivery, ETA: Today by 4:00 PM"
5. Customer Satisfied or Escalates
- Satisfied: Conversation ends, customer is happy
- Not satisfied: Customer asks for human agent, chatbot escalates
6. Human Agent Takes Over
A human agent picks up the conversation with full context:
- Conversation history
- Customer profile
- Order data
- Chatbot's summary
Chatbot Models
Msghub supports three AI models. Choose the one that fits your needs:
Claude (Anthropic)
- Best for: Nuanced conversations, complex reasoning
- Cost: Moderate
- Speed: Fast
- Accuracy: Very high
GPT-4 (OpenAI)
- Best for: General-purpose, versatile
- Cost: Moderate to high
- Speed: Fast
- Accuracy: Very high
Gemini (Google)
- Best for: Cost-effective, fast
- Cost: Low
- Speed: Very fast
- Accuracy: High
Switch Models
You can switch models anytime:
- Go Settings → AI Chatbot → Model
- Select your preferred model
- Click Save
No vendor lock-in—switch whenever you want.
Knowledge Base
The chatbot answers from your knowledge base. It doesn't make things up.
What Goes in the Knowledge Base
- FAQs — Frequently asked questions and answers
- Product Docs — Product features, specs, usage
- Policies — Return policy, shipping policy, privacy policy
- Guides — How-to guides, tutorials, troubleshooting
- Company Info — About us, contact info, hours
Upload Knowledge Base
- Go Settings → AI Chatbot → Knowledge Base
- Click Upload Documents
- Choose files:
- Word documents
- Text files
- Web pages
- Click Upload
Msghub will extract text and index it for the chatbot.
Add Knowledge Manually
- Go Settings → AI Chatbot → Knowledge Base
- Click Add Article
- Enter:
- Title — Article title
- Content — Article text
- Category — Optional category
- Click Save
Knowledge Base Quality
The better your knowledge base, the better the chatbot:
- Be specific — "Our return policy is 30 days" not "We have a return policy"
- Include examples — "You can return items within 30 days of purchase. For example, if you bought on April 1st, you can return until May 1st"
- Cover common questions — What are customers asking most?
- Update regularly — Keep information current
Chatbot Capabilities
Answer Questions
The chatbot answers questions from your knowledge base:
Customer: "What's your return policy?"
Chatbot: "We offer 30-day hassle-free returns. You can return items within 30 days of purchase. Just pack the item and drop it off at any shipping location. We'll process your refund within 5 business days."
Look Up Order Data
If you integrate your order system, the chatbot can look up real order data:
Customer: "Where's my order?"
Chatbot: "I found your order! Order #7842, Status: Out for Delivery, ETA: Today by 4:00 PM. Would you like me to send you a notification when it arrives?"
Qualify Leads
The chatbot can qualify leads for your sales team:
Customer: "I'm interested in your enterprise plan"
Chatbot: "Great! I'd love to help. Can you tell me:
- How many users do you need?
- What's your monthly message volume?
- What integrations do you need?"
Book Appointments
The chatbot can book appointments:
Customer: "I'd like to schedule a demo"
Chatbot: "Perfect! I can help with that. What date and time work best for you?"
Collect Information
The chatbot can collect customer information:
Customer: "I want to return my order"
Chatbot: "I can help with that. What's your order number?"
Customer: "ORD-4821"
Chatbot: "Got it. Can you tell me why you want to return it?"
Escalation to Human Agents
When the chatbot can't help, it escalates to a human agent.
Escalation Triggers
The chatbot escalates when:
- Customer asks for a human agent
- Chatbot can't find an answer in knowledge base
- Conversation requires human judgment (refunds, complaints)
- Customer is frustrated or angry
Escalation Message
Chatbot: "I understand you need help with a refund. Let me connect you with an agent who can help. One moment..."
Agent Handoff
The human agent receives:
- Full conversation history
- Customer profile
- Order data (if available)
- Chatbot's summary of the issue
The agent can see:
Conversation History:
- Customer: "I want to return my order"
- Chatbot: "I can help with that. What's your order number?"
- Customer: "ORD-4821"
- Chatbot: "I found your order from April 1st for $89.99. Why would you like to return it?"
- Customer: "The product is damaged"
Customer Profile:
- Name: Rahul
- Email: rahul@example.com
- Phone: +919876543210
- VIP: Yes
Order Data:
- Order #7842
- Date: April 1, 2024
- Amount: $89.99
- Status: Delivered
- Items: Sony Headphones
AI Summary:
Customer wants to return order due to damaged product. This is a legitimate return request. Recommend processing refund immediately.
Chatbot Across Channels
The same chatbot logic works across all 6 channels.
Customer: "Where's my order?"
Chatbot: "I found your order! Order #7842, Status: Out for Delivery, ETA: Today by 4:00 PM"
Web Chat
Customer: "What's your return policy?"
Chatbot: "We offer 30-day hassle-free returns!"
Customer: "Do you have this product in blue?"
Chatbot: "Yes! We have it in blue, red, and black. Which color would you like?"
SMS
Customer: "How do I track my order?"
Chatbot: "Reply with your order number and I'll send you the tracking link"
Instagram DM
Customer: "Is this product available?"
Chatbot: "Yes! It's in stock in all sizes. Would you like me to add it to your cart?"
RCS
Customer: "What's the price?"
Chatbot: "The Sony Headphones are $349. Would you like to buy now?"
Chatbot Performance
Resolution Rate
Msghub's chatbot resolves 80% of customer queries without human intervention.
This means:
- 80% of conversations end with the chatbot
- 20% escalate to human agents
- Agents handle only complex issues
Response Time
- Chatbot: 2 seconds average
- Human agent: 5-30 minutes average
The chatbot provides instant responses 24/7.
Satisfaction
Customers are satisfied when:
- The chatbot answers their question
- The response is accurate
- The chatbot escalates when needed
- Human agents are available when needed
Best Practices
Knowledge Base
- Be comprehensive — Cover all common questions
- Be accurate — Keep information current
- Be clear — Use simple language
- Be organized — Group related topics
Chatbot Responses
- Be helpful — Answer the question directly
- Be concise — Keep responses short
- Be friendly — Use a conversational tone
- Know when to escalate — Don't pretend to know
Training
- Review conversations — What questions does the chatbot struggle with?
- Update knowledge base — Add answers to common questions
- Test regularly — Ask the chatbot questions and check responses
- Iterate — Continuously improve
Troubleshooting
Chatbot not responding
- Check if enabled — Is the chatbot enabled in Settings?
- Check knowledge base — Does it have answers?
- Check flow — Is the chatbot flow configured?
Low resolution rate
- Check knowledge base — Does it cover common questions?
- Check accuracy — Are the answers correct?
- Check escalation — Is the chatbot escalating appropriately?
Inaccurate responses
- Check knowledge base — Is the information correct?
- Check model — Try a different AI model
- Check training — Provide more examples