Text Analytics & Classification at Scale

Placeholder case study — replace with your real project details.

The problem

Tens of thousands of free-text records (comments, descriptions, incident notes) needed consistent categorization to feed reporting. Manual tagging was inconsistent between people and never kept up with volume.

The approach

• Started with a classical baseline: TF-IDF features + linear models in scikit-learn, giving a fast, cheap, interpretable classifier.
• Used an LLM as a labeling assistant to bootstrap training data: the model proposed labels with confidence scores, humans only reviewed the uncertain ones.
• Measured everything: per-class precision/recall, confusion matrices, and drift checks on new data before trusting predictions in production.
• Packaged the pipeline so a scheduled job classifies new records nightly and writes results straight to the reporting layer.

The outcome

• Consistent, auditable categories across the full historical dataset.
• Labeling effort reduced to reviewing edge cases only.
• Category-level trends became a standard slide in monthly business reviews.