A synthetic dataset of 13,725 residential-maintenance tickets written the way real tenants write them — polite, panicked, passive-aggressive, or confused — each paired with operational metadata (category, urgency, assigned contractor, cost, resolution time).
Built for an end-to-end NLP pipeline: triage classification, similar-case retrieval (embeddings + FAISS), and work-order / reply generation.
