Inconsistent screening feedback — automate rubric-to-report AI for festivals

Festival programmers, jurors and filmmakers routinely cite inconsistent screening feedback as a major pain point: reviewers use different rubrics and produce narrative reports that are uneven, time-consuming to reconcile, and often unusable for selection or communication. That burden falls on festival operations—especially mid-sized events with small teams—who routinely spend hours normalizing comments, losing institutional memory and frustrating applicants. You could build an AI-powered rubric-to-report engine that ingests standardized rubric fields from submission platforms, applies configurable weighting and tone profiles, and emits coherent narrative reports, scorecards and batch summaries that plug into existing festival workflows and CRMs. The addressable market is meaningful: roughly 30,000 festivals globally at an achievable pricing around $30,000 ACV implies about $900M in TAM; a market score of 95/100 and revenue potential of 88/100 reflect low competition and strong demand driven by improved LLM output quality, hybrid/virtual festival workflows, and increasing standardization of submission metadata. To stand out, prioritize explainability and audit trails, modular integrations with major submission platforms and administrative tools, and a human-in-the-loop UX that preserves juror nuance while cutting editorial time. The core strengths are clear technical feasibility and a defined TAM; the main challenges are heterogeneous rubric formats, conservative purchasing behavior in arts organizations, and the need to prove fairness, privacy and reliability before wide adoption.