AI-powered Support
Operations Optimization
A gDATA-style support operations case study that uses public support data, ML/NLP, selective LLM fallback, and human-safe routing policies to automate case routing and turn model confidence into actionable operating recommendations.
The Business Challenge
Support teams face competing goals: automation coverage, human review load, LLM API cost, and routing quality. A principled system makes those tradeoffs explicit and measurable.
Reduce Manual Review Load
Manual triage of every incoming ticket is costly and doesn't scale. Automation needs to be safe enough to handle high-confidence cases without burdening agents.
Control LLM Invocation Cost
LLM-only routing invokes a language model for every ticket — expensive and hard to cost-control at scale. Smart routing limits LLM calls to truly ambiguous cases.
Preserve Routing Quality & Safety
Misrouted tickets harm customer trust and operations. Uncertain cases and LLM failures must always land in human triage — the system should never fail silently.
From Data to Decision
A structured pipeline that goes from raw public data to an actionable routing policy with explicit, measurable tradeoffs.
Why this maps to Business Data Science
Designed to show the full loop from ambiguous support-ops problem to data-driven recommendation.
Business Problem Framing
Balances manual triage cost, automation coverage, LLM invocation cost, and routing quality.
Data Science Implementation
Uses public support data, metadata-derived labels, supervised benchmark, and threshold-sweep analysis.
ML/NLP + LLM Solutioning
Combines keyword rules, TF-IDF/Logistic Regression, selective LLM classification, and human fallback.
Actionable Recommendation
Recommends a hybrid routing policy and confidence-threshold selection based on risk, review capacity, and LLM budget.
4-Stage Routing Cascade
Each stage handles the cases it's best suited for. Ambiguous tickets pass downstream; uncertain cases always reach a human.
Rule-based Routing
Deterministic pattern matching for obvious cases — instant, zero model overhead, highest reliability.
Calibrated ML
TF-IDF + Logistic Regression with isotonic calibration. Auto-routes when confidence ≥ high threshold.
LLM Fallback
LLM performs issue-type classification for ambiguous low-confidence tickets. Not invoked for every ticket.
Human Triage
Middle-confidence ambiguity and LLM failures always route here. The safety net — never fails silently.
Not an LLM-only design. LLM calls are intentionally limited to low-confidence ambiguous cases to control API cost and improve operational safety. The majority of tickets are handled by rules or ML without any LLM invocation.
Try the routing logic
Paste or select a sample support ticket to see how the hybrid routing policy would classify and route it.
Select a sample or type a ticket, then click "Route ticket"
Static front-end demo based on the project routing design. Full training, evaluation, and routing pipeline are available in the GitHub implementation.
Results & Insights
Three views of the system: operational health, policy tradeoffs, and measured model quality. Metric types are clearly distinguished.
Operations Overview
Shows routing-stage distribution, human triage rate, LLM invocation rate, average confidence score, estimated cost per ticket, and queue distribution across all processed tickets.
Human triage rate is a routing-system proxy, not a downstream escalation rate. Cost estimates are analytic, not measured from live LLM calls.
Cost–Coverage Policy Tradeoff
Shows how confidence thresholds shift tickets between auto-routing, LLM fallback, and human review. Thresholds are operating policy choices — not just model parameters — and must be selected with risk tolerance and LLM budget in mind.
Computed analytically from ML confidence scores. No live LLM calls required for this sweep.
Model Evaluation
Measured ML vs keyword baseline on 1,665 held-out metadata-derived tickets. Improved ML raises Macro-F1 from 12.1% to 33.2% vs keyword rules. ML baseline's 59.4% accuracy reflects majority-class bias; the improved model trades raw accuracy for better class balance. Limitations are transparent.
Labels are metadata-derived (Ticket Type field), not human-reviewed. Absolute scores remain modest and should be interpreted cautiously.
Use a hybrid routing policy
rather than LLM-only or rules-only routing.
Keep deterministic rules for high-signal obvious cases — no model overhead, highest reliability.
Use calibrated ML for high-confidence routing — cost-efficient, fast, and measurable.
Trigger LLM classification only for ambiguous low-confidence tickets — not for every case.
Route uncertain cases and LLM failures to human triage — never fail silently.
Select confidence thresholds based on risk tolerance, review capacity, and LLM budget.
Executive Decision Memo
What I would recommend to a support operations leader.
Use a hybrid routing policy rather than LLM-only routing.
Rules handle obvious cases cheaply; calibrated ML handles high-confidence routing; LLM fallback is reserved for ambiguity; human triage protects quality.
Choose confidence thresholds based on routing risk, agent review capacity, and LLM budget.
Build a human-reviewed eval set, add queue-level SLA requirements, and monitor calibration/drift before production rollout.
Stakeholder View
How the analysis translates for different partners in a support operations environment.
Support Ops
Use routing-stage and queue metrics to understand review load and staffing pressure.
Data Science
Evaluate ML against keyword baselines with measured accuracy, macro-F1, weighted-F1, and per-class metrics.
Engineering
Implement configurable thresholds, safe LLM fallback, and human-triage failure handling.
Leadership
Use cost–coverage tradeoffs to choose an operating policy aligned with risk tolerance and LLM budget.
Limitations & Next Steps
Transparent caveats are a mark of rigorous data science. Here is what this system does and does not claim.
Current Limitations
- Metadata-derived labels are not human-reviewed production gold labels
- Kaggle ticket descriptions are templated and may not fully reflect production support traffic
- The supervised benchmark covers 3 of 6 routing classes reliably present in the raw Kaggle metadata
- Absolute ML scores are modest; interpret cautiously against real-world benchmarks
Next Steps
- Build a 500–2,000 row human-reviewed eval set for more reliable per-class F1 signal
- Add queue-level SLA policies and risk-tier threshold settings per support category
- Track true downstream escalation and resolution outcomes beyond routing decisions
- Add calibration plots, reliability monitoring, and model drift detection
Tech Stack
Built entirely with open-source tools on public Kaggle data. No proprietary data sources.