AI Chatbot Deployment Checklist for Pakistani E-Commerce: 40-Point Guide

Deploying an AI chatbot on a Pakistani e-commerce store without this checklist is how businesses end up with a bot that confuses customers, fails during sales peaks, leaks personal data, and gets abandoned after two weeks. These 40 checkpoints — battle-tested across Karachi and Lahore e-commerce deployments — are the difference between a chatbot that becomes your best-performing sales channel and one that damages your brand.

Why Pakistani E-Commerce Chatbot Deployments Fail

The failure rate for AI chatbot deployments in Pakistani e-commerce is high — not because the technology does not work, but because deployments are rushed without proper planning. Common failure modes include: a chatbot that does not understand Roman Urdu (how 80% of Pakistani customers actually type), one that cannot answer basic product questions because the catalogue was never properly loaded, or one that cannot hand off to a human agent during customer escalations. This 40-point checklist eliminates all of these.

Section 1: Pre-Deployment Infrastructure (Points 1–10)

Point 1: Server capacity assessment
Your e-commerce hosting must be able to handle chatbot-related traffic alongside regular traffic. AI chatbots — especially those making real-time API calls to LLM providers — add latency to page loads if your server is already near capacity. If you are on shared hosting, upgrade to (/managed-wordpress-hosting) or a (/hosting) before deploying an AI chatbot.

Point 2: API rate limit planning
Determine your expected peak concurrent chatbot sessions. If your store gets 500 simultaneous visitors during a sale, you could have 50–100 simultaneous chatbot conversations. This translates to 50–100 simultaneous API calls to your LLM provider. Calculate your API tier limits and pre-purchase capacity headroom for peak events.

Point 3: Webhook reliability testing
If your chatbot integrates with WooCommerce via webhooks (for order status queries, stock checks, etc.), test webhook delivery reliability under load before going live. Failed webhooks mean customers receive wrong information about their orders.

Point 4: CDN configuration for chatbot widget
The chatbot JavaScript widget adds weight to your page. Ensure it loads asynchronously (not blocking your critical rendering path) and is served from a CDN. Every 100ms added to page load by the chatbot widget costs you the same conversion rate as any other slowdown.

Point 5: SSL certificate verification
All chatbot API communications must be over HTTPS. Verify your SSL certificate is valid, not expired, and covers any subdomains your chatbot uses.

Point 6: Database connection capacity
Chatbots that store conversation history (required for context-aware multi-turn conversations) make database writes on every message. Ensure your database server can handle the additional connection load without hitting the concurrent connection limits common on shared hosting.

Point 7: Staging environment testing
Deploy and test the chatbot on a staging version of your store before going live. Test every integration, every fallback path, and every escalation trigger.

Point 8: Load testing the integrated stack
Simulate 50 concurrent chatbot users alongside normal website traffic. Measure response times for both the chatbot and the underlying website. If either degrades significantly, address infrastructure before launch.

Point 9: Monitoring and alerting setup
Set up uptime monitoring for your chatbot API endpoint specifically. If the chatbot goes down, you need to know within 5 minutes, not 5 hours.

Point 10: Rollback plan
Have a one-click method to disable the chatbot widget without touching your site's core files. This lets you kill a misbehaving chatbot in under 60 seconds if needed.

Section 2: Language and Localisation (Points 11–18)

Point 11: Roman Urdu training examples
Pakistani customers predominantly type in Roman Urdu ("ye shirt XL mein hai?" "price kya hai?"). Your chatbot must have training examples in Roman Urdu for all common query types. Test at least 50 Roman Urdu representative queries before launch.

Point 12: Urdu script support
Some customers type in Urdu script. Your chatbot interface must render Urdu script correctly (right-to-left text direction, appropriate font). Test this explicitly — many chatbot widgets that work perfectly in English display broken characters with Urdu script.

Point 13: Urdu-English code-switching
Pakistani customers often mix languages within single messages: "Size guide share karo please" or "Can you tell me delivery time for Lahore?" Your chatbot must handle code-switching gracefully without asking customers to repeat themselves in a single language.

Point 14: Colloquial product terminology
Pakistani customers use colloquial names for product categories that differ from formal English. "Shalwar kameez" not "traditional dress", "chappal" not "sandal", "duppatta" not "scarf." Map these terms in your chatbot's product discovery logic.

Point 15: City-specific delivery terminology
References to "Karachi delivery", "Lahore wale", "Islamabad me free shipping" must be understood and answered correctly. Pre-load city-specific delivery rules into your chatbot's knowledge base.

Point 16: Currency and pricing format
All prices displayed by the chatbot must be in PKR. Test that the chatbot never displays USD or other currencies. Verify price formatting is consistent with your website (PKR 1,200 vs Rs.1200 vs 1200/- — pick one and enforce it).

Point 17: Cultural sensitivity review
Have a Pakistani native speaker review all chatbot responses for cultural appropriateness. Tone, formality level, and phrasing conventions differ significantly between Pakistani and Western English communication styles.

Point 18: Greeting and farewell scripts in Urdu
Have at least one Urdu greeting option ("Assalamu Alaikum! Kaise madad karun aapki?") and appropriate Urdu farewell messages. This small touch dramatically increases trust with Urdu-first users.

Section 3: Product Catalogue Training (Points 19–24)

Point 19: Full catalogue sync
Your AI chatbot must have access to your complete, current product catalogue — not a sample. For WooCommerce stores, use the WooCommerce REST API to sync product data. Verify the sync covers: product names, SKUs, descriptions, prices, available sizes/variants, stock status, and images.

Point 20: Out-of-stock handling
Test what happens when a customer asks about an out-of-stock product. The chatbot must clearly communicate unavailability and proactively offer alternatives from the same category, not simply say "not available."

Point 21: Category navigation
Customers often say "show me your blue dresses" or "I'm looking for something under PKR 3,000." Test at least 20 attribute-based product queries and verify accurate results.

Point 22: New arrival and promotional awareness
Connect your chatbot to your promotions data. If you have a Eid Sale running, the chatbot must know about it and proactively mention it to browsing visitors.

Point 23: Cross-sell and upsell logic
Define and test cross-sell rules: when a customer asks about a kurta, does the chatbot suggest matching trousers? When asking about a phone, does it suggest compatible accessories? These rules must be explicitly configured, not assumed.

Point 24: Image display in chatbot
Verify that product images display correctly within the chatbot widget across mobile and desktop. Broken images in the chatbot's product discovery responses are a significant trust signal.

Section 4: Payment and Order Integration (Points 25–29)

Point 25: JazzCash integration testing
If your store accepts JazzCash, test that the chatbot can correctly answer questions about JazzCash payment process, confirm JazzCash as a payment option, and link to the correct payment flow.

Point 26: EasyPaisa integration testing
Same as JazzCash — verify all EasyPaisa-related responses are accurate and the payment flow links work.

Point 27: Bank transfer instructions
For COD and bank transfer orders, verify the chatbot provides accurate account details and instructions. Test that these details are drawn from a single source of truth, not hardcoded, so updates propagate automatically.

Point 28: Order status query accuracy
The most common chatbot query for e-commerce is "where is my order?" Test this flow rigorously: the chatbot must correctly retrieve order status from WooCommerce using the customer's order number or registered phone number, and provide accurate status in under 3 seconds.

Point 29: Return and refund policy delivery
Verify the chatbot can accurately explain your full return policy, including time limits, product condition requirements, and refund processing times. Inaccurate return policy information from chatbots creates legal and customer experience problems.

Section 5: Human Handoff (Points 30–34)

Point 30: Escalation trigger configuration
Define and test all escalation triggers: keywords indicating anger or frustration, queries the bot answers with low confidence, specific topic categories (legal complaints, damaged goods, fraud), and explicit "speak to human" requests.

Copy// Example escalation trigger configuration
{
  "escalation_triggers": {
    "keywords": ["fraud", "cheated", "police", "court", "stolen", "fake"],
    "sentiment_threshold": -0.6,
    "low_confidence_threshold": 0.4,
    "explicit_requests": [
      "human agent", "real person", "manager",
      "insaan se baat", "staff se connect"
    ]
  },
  "escalation_action": "transfer_to_whatsapp_with_transcript"
}

Point 31: Agent notification system
When the chatbot escalates, the receiving human agent must be notified immediately with: full conversation transcript, customer contact details, and the reason for escalation. Test this flow end-to-end.

Point 32: Off-hours escalation handling
Define what happens when a customer needs human help outside business hours. Options: schedule a callback, send an email to customer service, or offer a WhatsApp message that an agent will respond to when available. Never leave a customer with a dead-end "no agents available" message.

Point 33: WhatsApp Business API integration
For Pakistani e-commerce, WhatsApp is the primary customer communication channel. Verify your chatbot can initiate a WhatsApp conversation thread seamlessly, passing full context from the web widget conversation.

Point 34: SLA definition and monitoring
Define and monitor escalated conversation SLAs: maximum response time for human agent pickup after escalation. 15 minutes during business hours is a reasonable upper limit. Set up automated alerts if escalated conversations are not picked up within the SLA window.

Section 6: Privacy and Compliance (Points 35–37)

Point 35: Data retention policy implementation
Define how long conversation logs are retained and where they are stored. Under Pakistan's evolving PDPA framework, customers have a right to expect reasonable data handling. Conversation data containing personal information (names, addresses, order details) should not be retained indefinitely.

Point 36: Privacy disclosure to users
Your chatbot must inform users at the start of a conversation that it is AI-powered and that conversation may be stored. A simple introductory message: "Hi! I'm an AI assistant. Our conversation may be saved to improve service. " satisfies this.

Point 37: Third-party data sharing review
If your chatbot sends conversation data to an LLM API provider (OpenAI, Gemini), review whether this constitutes a data processing arrangement under applicable frameworks. For stores processing Pakistani customer data, document your data flow.

Section 7: Performance Monitoring and 30-Day Review (Points 38–40)

Point 38: KPI baseline and tracking
Before launch, record baseline metrics: current customer support ticket volume, average resolution time, cart abandonment rate, and conversion rate. After launch, track: chatbot resolution rate (queries resolved without human escalation), average conversation length, and post-chat conversion uplift.

Point 39: Conversation quality audit
Weekly: review 25 randomly sampled chatbot conversations. Look for: wrong answers, missed escalation opportunities, language quality issues, and product discovery failures. Feed findings back into training data improvements.

Point 40: 30-day optimisation review
At day 30, conduct a structured review against your baseline metrics. A well-deployed AI chatbot on a Pakistani e-commerce store should show: 40–60% reduction in routine support queries to human agents, measurable cart recovery (5–15% of conversations that started at cart page), and maintained or improved conversion rate. If these benchmarks are not met, audit against this checklist for missed items.

Deploying With Professional Support

Pakish.net's (/ai-automation) team has deployed AI chatbots for Pakistani e-commerce businesses in Karachi, Lahore, and Islamabad. We handle the full stack: chatbot platform selection, WooCommerce integration, Roman Urdu training, WhatsApp Business API setup, and post-launch monitoring. Our (/ai-implementation) service includes a 30-day optimisation period to ensure your chatbot reaches its performance benchmarks. (https://my.pakish.net/submitticket.php?step=2&deptid=1) for a scoping conversation.