Intelligent_Ticket-Routing-SLA-System

Intelligent Multi-Channel Ticket Routing & SLA Management System

Routes customer cases from email, chat and phone to the right agent using explainable rules (intent, priority, language, skills, shifts, capacity, queue load) and tracks SLA deadlines on real business calendars. Python 3.11/3.12, SQLite, Pandas, Streamlit, Plotly, Pytest. No paid APIs.

Open in Streamlithttps://intelligentticket-routing-sla-system.streamlit.app/

View the dashboard

This application is a Python Streamlit server, not a static website. GitHub Pages therefore displays repository files such as this README and cannot run app.py.

To deploy the interactive dashboard:

  1. Open Streamlit Community Cloud.
  2. Sign in with the GitHub account that owns this repository.
  3. Select Create app.
  4. Choose repository Kanyaraashi/Intelligent_Ticket-Routing-SLA-System, branch main, and main file path app.py.
  5. Deploy the app.
  6. Replace the placeholder Streamlit badge link above with the public application URL after deployment.

Why it matters

Wrong routing, overloaded agents, missed deadlines and ticket ping-pong cost money and customers. This project demonstrates a deterministic, auditable alternative with full history and automatic anomaly detection.

Quick start (Windows)

  1. Double-click INSTALL.bat 2. GENERATE_STARTER_DATA.bat 3. RUN_DASHBOARD.bat -> open http://localhost:8501
    Tests: RUN_TESTS.bat. Validation: VALIDATE_DATA.bat. Report: CREATE_REPORT.bat. Full data (30,000 tickets): GENERATE_FULL_DATA.bat. Read START_HERE.md first if you are a beginner.

What is included

Schema (13 entities + bridges) - deterministic generator (seed 20261022; starter 500 tickets/20 agents/2,000 messages/7 days; full 30,000/100/100,000/90 days) - routing engine - SLA engine - lifecycle rules - 5 injected anomalies + detector - 17 validation check families - KPI engine - 9-page dashboard - 42 tests - CSV/JSON exports - reproducibility proof.

Dashboard pages

Executive Overview - Ticket Explorer - Intelligent Routing (read-only simulation with agent eligibility and rejection reasons) - Agent Performance - SLA Monitoring - Queue Analytics - Anomaly Detection - Data Validation - About.

Docs

docs/: DATA_DICTIONARY, DATABASE_DESIGN, ER_DIAGRAM (Mermaid), BUSINESS_RULES, ARCHITECTURE, TESTING, AI_USAGE, PROMPT_HISTORY, LIMITATIONS, VALIDATION_REPORT. Also FINAL_ACCEPTANCE_REPORT.md.

Screenshots

No need for Screenshots since the Streamlit cloud link is provided at top of the readme file, from there you can see the full project overview and UI.

AI disclosure

Code was generated by Claude; see docs/AI_USAGE.md.

Layout

app.py, config/, database/schema.sql, src/, scripts/, tests/, data/ (databases + ground truth), exports/ (CSV+JSON+manifest), docs/.