SaaS & Automation · Case Study 03

How We Automated 80% of
Support Tickets in 30 Days

A logistics SaaS company was bleeding $7,200/month on support staff. 80% of tickets were repetitive queries. We built a custom NLP WhatsApp bot that handled them automatically, saving $4,800/month in the first 30 days.

Get Results Like These Read the case study
Built in 30 days ROI paid back in week one Bot answers 24/7
80%Tickets Automated
$4.8k/moCost Savings
<3 secResponse Time
94%Satisfaction Score
Built withWhatsApp APIOpenAI NLPWebhooksNode.jsPythonDockerMongoDBRedis
<3 secResponse time, was 4 hrs
$4.8k/moSaved in the first 30 days
94%Satisfaction score
01 The Challenge

The Problem

1,200+ support tickets per month. Three full-time support agents costing $7,200/month combined. Average response time was 4 hours. Customers were churning because they couldn't get answers fast. The worst part: 80% of tickets were repetitive queries that a bot could answer instantly: “Where is my shipment?”, “How do I track my order?”, “Why was my delivery delayed?”

1,200+Support tickets per month
$7,200Monthly cost of three full-time agents
4 hrsAverage response time
02 Our Approach

AI + Human Collaboration

AI analyzed 6 months of ticket data to identify the top 40 query patterns. Engineers built a WhatsApp bot with NLP trained on actual customer conversations.

  1. 01 · Channel

    Customer on WhatsApp

    Asks about a shipment, an order or a delay, any time of day.

  2. 02 · NLP

    AI bot

    NLP trained on actual customer conversations. The bot handles the rest 24/7.

  3. 03 · Live data

    Logistics API

    The bot integrated directly with the logistics API for real-time shipment data.

  4. 04 · Escalations only

    Human agent

    Complex or emotional cases that need empathy.

6 monthsOf ticket data analyzed by AI
40Top query patterns identified
24/7Bot coverage, humans on escalations
03 The Process

4 Phases of Automation

From six months of support tickets to a live WhatsApp bot, built in 30 days.

  1. 01
    Data Mining

    Ticket Analysis

    Analyzed 6 months of support tickets. Identified top 40 query patterns. Grouped by category: tracking, billing, technical, escalation.

  2. 02
    AI Training

    NLP Training & Bot Build

    Trained NLP model on 1,200+ customer messages. Built WhatsApp bot with intent recognition and API integration for real-time data.

  3. 03
    QA & Launch Prep

    Integration & Testing

    Integrated bot with logistics API for live shipment tracking. Tested 200+ edge cases. Set up fallback to human agents.

  4. 04
    Go Live

    Launch & Monitoring

    Deployed bot to WhatsApp. Monitored conversations, accuracy, and customer satisfaction. Continuous retraining based on new data.

05 Technical Stack

Built with AI & APIs

Two layers: the bot that understands every query, and the integrations that connect it to customers and live shipment data.

Group 01 · Bot engine

AI

Intent recognition, training data and the engine the bot runs on.

  • OpenAI NLPIntent recognition trained on 1,200+ customer messages
  • PythonNLP training and continuous retraining on new data
  • Node.jsBot backend and conversation handling
  • MongoDBTicket and conversation data
  • RedisFast caching for the bot’s replies
Group 02 · Connections

APIs & Integrations

How the bot reaches customers, live shipment data and the team.

  • WhatsApp APIThe channel customers use, answered 24/7
  • Logistics APIReal-time shipment data for live tracking
  • WebhooksReal-time message and data events
  • DockerContainerised deployment for go-live
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