How Haptik Is Transforming Customer Support in 2025

A Beginner’s Guide to Building Chatbots with Haptik—

Building chatbots can seem intimidating at first, but with platforms like Haptik it becomes accessible to non-technical users and developers alike. This guide walks you through the essentials: what Haptik is, why use it, planning your bot, step-by-step setup, designing conversational flows, integrating with systems, testing and deployment, and best practices to make a chatbot that users love.

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What is Haptik?

Haptik is a conversational AI platform that enables businesses to design, build, and deploy chatbots across messaging channels (web chat, mobile apps, WhatsApp, Facebook Messenger, etc.). It combines natural language understanding (NLU), dialogue management, and integrations to automate customer support, lead generation, transactions, and more.

Key benefits: easy visual flow builder, multilingual NLU, prebuilt templates, analytics, and enterprise integrations.

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Why choose Haptik?

• Rapid time-to-market with drag-and-drop builders and templates.
• Strong NLU for intent recognition and entity extraction.
• Omnichannel support (WhatsApp, web, in-app, social).
• Enterprise-ready features: CRM/ERP integrations, security, and analytics.
• Scalability to handle high volumes of concurrent conversations.

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Plan your chatbot before building

1. Define objectives: customer support, lead capture, FAQs, sales, booking, etc.
2. Identify target users and channels (web, WhatsApp, app).
3. Map common user journeys and prioritize top tasks (e.g., order tracking, password reset).
4. List required integrations (CRM, payment gateway, knowledge base).
5. Decide on bot personality and tone—consistent and aligned with brand.
6. Define success metrics: containment rate, resolution time, handover rate, CSAT.

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Haptik account and environment setup

1. Sign up for Haptik and choose the appropriate plan (trial, startup, enterprise).
2. Access the Haptik console (dashboard) and create a new bot project.
3. Configure basic settings: bot name, default language, time zone, and channel endpoints.
4. Add team members and roles (developer, designer, analyst) with appropriate permissions.

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Designing conversational flows

Haptik provides both visual flow builders and NLU-driven approaches. Use a hybrid approach for robustness.

• Use visual flows for guided interactions (menus, forms, multi-step processes).
• Use NLU for open-ended queries and intent handling.
• Combine flows and NLU: detect intent, then route into structured flow for task completion.

Tips:

• Start with a welcome message and quick-reply options.
• Keep prompts short and provide clear choices.
• Use fallback responses and escalate to human agents when confidence is low.
• Implement slot-filling for collecting structured data (name, email, order ID).

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Creating intents and entities

1. Define intents for user goals (e.g., “track_order”, “cancel_order”, “product_info”).
2. Provide diverse training utterances for each intent (at least 10–20 varied examples).
3. Create entities (order_id, date, product_name) and annotate sample utterances.
4. Test and iterate: use Haptik’s training console to review NLU performance and retrain as needed.

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Building a simple example: Order Tracking bot

1. Create intent: track_order.
2. Add training utterances: “Where is my order?”, “Track order 12345”, “Order status”.
3. Create entity: order_id (pattern: numeric/alphanumeric).
4. Build flow:
   • Welcome -> Ask for order ID (quick reply or text input).
   • Validate order ID format; call backend API to fetch status.
   • Show status with options: “More details”, “Speak to agent”, “Back to main menu”.
5. Add error handling: if API fails, show friendly message and offer human handover.

Include code/webhook example (Haptik calls your webhook). Example Node.js webhook handler:

// Example Express webhook to handle Haptik's request for order status const express = require('express'); const fetch = require('node-fetch'); const app = express(); app.use(express.json()); app.post('/haptik/webhook', async (req, res) => {   const { order_id } = req.body; // adapt to Haptik's payload structure   try {     const apiRes = await fetch(`https://api.yourshop.com/orders/${order_id}`);     if (!apiRes.ok) throw new Error('Order not found');     const order = await apiRes.json();     // Respond in format Haptik expects     return res.json({       response: `Order ${order.id} is currently: ${order.status}`,       actions: [{ type: 'quick_replies', options: ['More details', 'Speak to agent'] }]     });   } catch (err) {     return res.json({ response: 'Sorry, I could not find that order. Want to try again or contact support?' });   } }); app.listen(3000, () => console.log('Webhook listening on port 3000')); 

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Integrations

• CRM (Salesforce, HubSpot): sync leads and user data.
• Payment gateways: collect payments inside chat (where supported).
• Knowledge bases: surface articles for self-service.
• Authentication: verify users via OTP or SSO.
• Live-agent handover: route conversations to human agents with context.

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Testing and training

• Use Haptik’s test console to simulate conversations.
• Run edge-case tests and low-confidence scenarios.
• Monitor NLU confusion matrix and retrain intents regularly.
• A/B test messages, prompts, and flows to improve containment and CSAT.

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Deployment and monitoring

• Deploy to chosen channels; comply with channel-specific rules (WhatsApp Business API approvals).
• Monitor analytics: containment rate, fallback rate, average handling time, CSAT.
• Set alerts for spikes in handovers or failures.
• Keep a changelog for flow updates and model retraining.

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Best practices

• Design for interruptions and resumability (save conversation state).
• Offer graceful human handover with context.
• Use quick replies and carousels to reduce typing.
• Respect user privacy and store only necessary PII.
• Localize for language and cultural nuances.
• Maintain a small set of high-quality intents rather than many overlapping ones.

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Common pitfalls and how to avoid them

• Over-reliance on open NLU without structured fallbacks — use guided flows for critical tasks.
• Insufficient training data — collect and expand utterances from real conversations.
• Ignoring analytics — use data to continuously improve.
• Poor error messages — always provide next steps.

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Next steps and learning resources

• Start with a simple FAQ or order-tracking bot.
• Collect conversation logs (anonymized) to expand training data.
• Explore Haptik templates and case studies for inspiration.
• Practice building integrations and handling edge cases.

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Building chatbots on Haptik becomes easier with iteration: start small, measure frequently, and expand capabilities based on real user behavior.