The Future of Support Automation

Smart-Ticket-Classifier

Instantly route, prioritize, and resolve customer support tickets with unparalleled AI accuracy. Reduce MTTR and increase FCR across your service desk operations.

See Features in Action

The Classification Challenge

Slow Triage

Manual review leads to long wait times and delayed first response (MTTR).

Inaccurate Routing

Human error causes tickets to be miscategorized or routed to the wrong team.

High Workload

Agents spend valuable time manually tagging, rather than solving problems.

The Smart-Classifier Workflow

1. Ingestion

New support ticket received via email or API.

2. Preprocessing

data_connector.py cleans, tokenizes, and preps the text.

3. Prediction

categorizer.py uses DistilBERT for category/severity.

4. Recommendation

SBERT provides a Real-Time Solution Suggestion.

5. Output/Loop

Results are displayed (GUI/Slack) and feedback loop closes.

The Core Tech Stack

System Architecture Layers

DEPLOYMENT & ANALYTICS

Streamlit (Interactive Dashboard/GUI), Flask (API Endpoint), Matplotlib (Visualization), Slack Webhooks (Notifications).

CORE INTELLIGENCE

DistilBERT (Classification Model), SBERT (Semantic Search/Recommendation Engine), PyTorch (Deep Learning).

DATA & FOUNDATION

Python (Programming Language), Pandas, NumPy (Data Processing & Cleaning).

Why Use This Project

Efficiency & Speed
Automates triage to achieve up to **80% reduction in MTTR**, allowing agents to focus on high-touch issues.
Unmatched Accuracy
Leverages DistilBERT for **95%+ classification accuracy**, minimizing routing errors compared to legacy systems.
Proactive Insights
Features **Content Gap Detection** and real-time model analytics, driving continuous improvement in your knowledge base.

Key Outcomes of the 3.0 System

80%

Reduced MTTR

Lightning-fast triage ensures tickets are in the right hands within seconds, minimizing downtime.

95%+

Accuracy Score

Multi-factor models virtually eliminate routing errors caused by manual review and miscategorization.

Clean Data

Consistent, machine-driven classification improves historical data for better reporting and trend analysis.

Agent Empowerment

Frees up agents to focus on complex problem-solving, not routine categorization tasks.

Features in Action: The V3.0 Dashboard

Display Settings

Column Visibility

Content Gap

Filter by Predicted Severity

Critical High Medium Low N/A

AI-Powered Support Ticket Classifier

Instantly predict ticket Severity and Assigned Team,Top Suggestions and Real-Time-Solution

Simulate Incoming Ticket

Drag and drop file here

Limit 200MB per TXT, CSV

OR Paste Single Ticket Description Here

Interactive Analysis Summary

ID Ticket Snippet Severity Assigned Team Action
#14 CRM application not launching, entire sales team blocked... Critical (96.7%) Application Support Show Recommended Solution
Recommended Solution (Ticket #14)

Top Match: CRM application not launching

Confidence: 98.63%

Resolution Steps: 1. Verify user's 'Sales' security group. 2. Clear application cache on Citrix. 3. Escalate to AppDev if issue persists.

Model Prediction & Knowledge Base Analytics

Real-Time Content Gap Analysis

0.50%

Content Gap Ratio

202

Total Tickets Analyzed

1

Tickets Flagged as Gap

201

Successful KB Matches

Content Gap Analysis (Log History)
Content Gap
Successful Match

Total Tickets Analyzed: 202 (Gap Ratio: 0.5%)

Distribution of Predicted Issue Types
Application Support
Networking
Desktop Support
Security & Access
Predicted Severity Distribution
Critical (20%)
Low (80%)