The Problem

Support teams spend 70% of their time on repetitive tasks: reading tickets, determining urgency, classifying issues, and drafting boilerplate responses. High-volume businesses receive hundreds of tickets daily, but most follow predictable patterns.

Manual triage creates bottlenecks. Urgent issues get buried in the queue, sentiment goes undetected, and response times suffer. Customers expect instant acknowledgment—but human agents can't scale infinitely.

The Solution

Smart-Triage uses transformer-based NLP models (BERT, RoBERTa) to instantly classify tickets by category, detect sentiment, and generate context-aware response drafts. The system processes tickets in under 100ms, routing urgent issues to human agents while auto-resolving routine requests.

triage_engine.py 
from transformers import pipeline
from fastapi import FastAPI, BackgroundTasks
import asyncio

app = FastAPI()

# Load pre-trained models
sentiment_classifier = pipeline(
    "sentiment-analysis",
    model="distilbert-base-uncased-finetuned-sst-2-english"
)
category_classifier = pipeline(
    "zero-shot-classification",
    model="facebook/bart-large-mnli"
)

async def analyze_ticket(ticket_text: str):
    """Analyze ticket sentiment and category in parallel"""
    sentiment_task = asyncio.create_task(
        asyncio.to_thread(sentiment_classifier, ticket_text)
    )
    
    categories = ["Billing", "Technical", "Feedback", "Account"]
    category_task = asyncio.create_task(
        asyncio.to_thread(category_classifier, ticket_text, categories)
    )
    
    sentiment, category = await asyncio.gather(
        sentiment_task, category_task
    )
    
    return {
        "sentiment": sentiment[0]['label'],
        "confidence": sentiment[0]['score'],
        "category": category['labels'][0],
        "draft_reply": generate_response(sentiment, category)
    }

The system maintains a knowledge base of successful resolutions, learning from agent feedback to improve draft quality over time. Integration with existing ticketing systems (Zendesk, Intercom) takes minutes via REST APIs.

Key Features

  • Real-time Sentiment Analysis: Detects frustrated customers and flags urgent cases for immediate human attention
  • Multi-category Classification: Routes tickets to the right team (Billing, Technical, Sales) automatically
  • Context-aware Responses: Generates personalized drafts based on ticket history and customer profile
  • Escalation Logic: Complex or high-emotion tickets bypass automation and go straight to senior agents
  • Performance Metrics: Tracks resolution rate, response time, and customer satisfaction per category

Impact

In a 30-day pilot with a SaaS company processing 1,200 tickets/month, Smart-Triage achieved 60% auto-resolution rate. Average response time dropped from 4 hours to 12 minutes. Customer satisfaction scores improved by 18%.

Support agents reported spending less time on "copy-paste" responses and more time solving complex problems. The team reduced overtime hours by 40% while handling 2x the ticket volume.