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🧠 Reducing Chat Drop-Offs with AI-Powered Clarification Suggestions

🏢 Client Overview

A customer-centric business using WhatsApp Business API for chatbot interactions faced communication breakdowns due to unclear or ambiguous user messages.
This often led to:

• Incorrect bot replies
• Frustrated users
• High chat drop-off rates

🚨 The Challenge

In real-world chats, not every user input is clear. The previous system guessed user intent, resulting in:

• ❌ Irrelevant responses
• 😡 User frustration and chat abandonment
• 📈 Increased load on live agents

The need: A way to improve bot accuracy without adding friction.

⚙️ Our Solution

Atlas Inspire built an AI-powered clarification agent — a smart layer that analyzes the clarity of user inputs before the chatbot replies.

How it works:

• 🧪 Clarity Scoring: AI model scores messages from 0–100 based on clarity
• 🧠 Smart Routing:
  • Clarity ≥ 70: Proceed with bot’s direct reply
  • Clarity 30–70: Present multiple interpretations →
    “Did you mean A, B, or something else?”
  • Clarity < 30: Ask user to rephrase or provide more detail

Result: Smoother, more accurate conversations.

📈 Technologies Used

• OpenAI (GPT models)
• LangChain
• WhatsApp Business API
• Bhasini API (for multilingual input)
• MongoDB
• Python

✅ The Impact

• 🔻 50% drop in chat abandonment caused by vague replies
• 🎯 Higher response accuracy and user satisfaction
• 🧑‍💻 Lower human agent burden by reducing escalations
• 🌐 Better multilingual support using Bhasini

This AI enhancement transformed vague conversations into structured, efficient dialogues — elevating the customer experience while keeping costs optimized.