OVATION: Multi-Agent Review Response System

Responding to customer reviews quickly, consistently, and empathetically across Google, Yelp, and TripAdvisor is a constant operational burden for hospitality and service businesses. Generic templates feel impersonal, manual responses don't scale, and a single tone-deaf reply can trigger real reputational damage — yet most automation tools treat every review the same way, with no quality control or escalation path for high-stakes cases. OVATION solves this with a coordinated team of six specialized AI agents — Monitor, Triage, Research, Drafter, QA, and Escalation — built on Band's room-based multi-agent messaging. Each agent runs as an independent Python process with a clearly scoped job: Monitor watches for incoming reviews, Triage classifies sentiment and urgency, Research gathers relevant context (business policies, prior interactions, platform norms), Drafter writes a tailored response, QA evaluates it against nine concrete checks (character limits, empathy, legal safety, brand voice, and more), and Escalation routes anything risky to a human. Every handoff travels through Band's @mention-based routing as structured JSON envelopes, so agents share state and context instead of operating in isolation. To keep the system affordable to run, LLM usage is tiered: premium DeepSeek-V4 (via AI/ML API) powers the higher-stakes Drafting and QA steps, while cheaper Featherless-hosted models handle Triage, Research, and Escalation, and Monitor requires no LLM at all. A live dashboard visualizes the entire pipeline in real time — reviews moving through a kanban-style board, QA scores and check-by-check breakdowns in a detail drawer, and a scenario selector that lets judges trigger five distinct review types on demand, including a full QA revision loop and an end-to-end human escalation path.