Customer Support Intelligence Platform

Support-operations decision-support prototype for SLA monitoring, ticket-level breach-risk scoring, and prioritized follow-up.

Live demo: https://customer-support-intelligence-platform.streamlit.app/

What It Implements

• Streamlit KPI dashboard and ticket explorer
• Data-cleaning and feature-engineering pipeline that excludes target-defining resolution time from model inputs
• Random Forest SLA-breach classifier
• Ticket-level risk scores and deterministic recommended-action groups
• Model-performance and feature-importance views
• CSV export for filtered records and prediction results
• Tests and GitHub Actions validation

Evidence Boundary

The repository uses fictional demonstration data. Its metrics show that the pipeline executes correctly on that controlled dataset; they are not evidence of production performance.

Recommended actions are deterministic labels derived from model risk scores. The project does not currently connect to a live CRM, automatically update tickets, or execute an autonomous agent.

Architecture

flowchart LR
    A[Fictional support-ticket CSV] --> B[Cleaning and feature engineering]
    B --> C[Random Forest training]
    C --> D[Risk scores]
    D --> E[Deterministic action groups]
    E --> F[Streamlit review and CSV export]

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Run Locally

python -m venv .venv
python -m pip install -r requirements.txt
python src/data_cleaning.py
python src/model.py
streamlit run app/streamlit_app.py

Verify

python -m pip install pytest
pytest -q
python -m compileall app src tests

Limitations

• Fictional, simplified dataset
• No live CRM or ticketing-system integration
• No authentication or role-based access
• No automated alerts or ticket updates

The separate AI Ops Workflow Automation Platform demonstrates agent orchestration, tools, tracing, evaluation, and approval controls.