Quilty, an artificial intelligence startup specializing in script analysis, claims its platform can accurately forecast a film’s commercial success by evaluating unproduced screenplays. Founded by producers Simon Horsman and Daniel Wood, the service assigns scripts a numerical score from zero to one hundred, assessing narrative quality, estimated production costs, audience resonance, and cultural relevance. The founders position the tool as a development aid designed to democratize studio decision-making, allowing independent creators to gauge market viability while keeping human professionals in the final approval loop. Technically, Quilty operates as an integration layer rather than a proprietary machine learning model. The platform routes uploaded scripts through a combination of publicly available large language models, including Gemini for structural analysis, DeepSeek for financial modeling, and Claude alongside ChatGPT for character and narrative evaluation. Sentiment metrics are derived from the open-source VADER algorithm. Founder Wood emphasizes that the system relies on context prompting to minimize hallucinations, arguing that the modular architecture allows seamless updates as newer base models enter the market. The service charges fifty dollars per individual analysis, with volume discounts available for production teams. Despite its technical framework, Quilty’s predictive reliability has drawn industry skepticism following high-profile mismatches. The platform assigned a higher success probability to the biographical drama Christy, which grossed approximately two million dollars, over the fantasy blockbuster Sinners, which became an Oscar-winning box office phenomenon earning three hundred seventy million dollars. Executives attribute the discrepancy to star power calculations and production budget differentials, acknowledging that algorithmic assessments cannot factor in unpredictable variables such as actor controversies or viral cultural phenomena. The startup maintains that its sentiment engine and pattern recognition capabilities provide valuable developmental scaffolding rather than definitive financial guarantees. Horsman and Wood contend that traditional studio development relies on equally subjective human intuition, positioning Quilty as a standardized analytical supplement. Nevertheless, industry observers note that the platform functions primarily as a sophisticated mimicry tool, leveraging existing consumer-grade AI rather than demonstrating novel creative forecasting. As independent filmmakers and studios experiment with algorithmic development pipelines, Quilty represents a broader industry trend toward AI-assisted content evaluation. While the founders continue to advocate for data-driven greenlight strategies, the company’s inability to consistently predict market reception underscores the persistent gap between computational pattern matching and the nuanced cultural factors that drive cinematic success.