CinePrompt AI

Company: CinePrompt AI

Self-reported basis: The description is entirely self-reported by the author, unverified, and lacks any evidence of revenue, customers, or traction.

What it appears to be: A tool that processes YouTube videos into scene-by-scene technical breakdowns and production-ready AI video prompts, built using Google Opal and OpenAI technologies.

What changed: The project was submitted as part of the OpenAI 2026 hackathon, with a focus on AI video analysis and prompt engineering.

Most important open question: Is there any evidence that this tool has been used beyond the hackathon context, or whether it has evolved into a product with real-world adoption?

The description states that CinePrompt AI:

• Takes an uploaded video and analyzes it into a detailed scene-by-scene report.
• Extracts the transcript and identifies what is happening in each scene.
• Explains technical production details such as visuals, camera style, lighting, motion, sound, and scene structure.
• Converts that analysis into production-ready prompts for AI video generation in formats like 9:16 vertical and 16:9 landscape.

Inference: The product appears to be a video analysis tool designed for creative professionals or AI video makers who want structured breakdowns of videos. It is not a general-purpose video editor or transcription tool.

The author states:

• CinePrompt AI was inspired by the need to speed up and improve the process of manually breaking down videos.
• The tool aims to make it faster, cleaner, and more useful with AI.
• It helps creators, editors, educators, and AI video makers study structure and generate prompts for new AI video creation.

Inference: The positioning is that of a productivity tool for content creators and AI video engineers. It is positioned as an enhancement to manual workflows, not a replacement.

The description states:

• The target users are creators, editors, educators, and AI video makers.
• These users watch videos and manually break them down before reusing the idea or studying structure.

Inference: The ICP appears to be content creators or technical users who work with video production and AI tools. However, no evidence of specific customer segments or personas is provided.

Not evidenced.

Explanation: There is no mention of pricing, monetization strategy, or business model in the description.

The description states:

• The app was built in Google Opal as an AI workflow.
• It uses Codex with GPT-5.6 during OpenAI Build Week.
• The project includes a GitHub companion with setup instructions, sample data, export logic, validation tests, and a production-pack interface.

Inference: The tool is built using AI workflows and prompt engineering tools. It has a basic technical structure, but no evidence of scalability or production deployment beyond the hackathon submission.

Not evidenced.

Explanation: There is no evidence of users, adoption, revenue, or product usage beyond the hackathon submission. The project is described as a prototype or proof-of-concept.

Not evidenced.

Explanation: No mention of competitors or market context in the description. The author does not reference existing tools or platforms that do similar work.

• No traction or adoption: The project is described as a hackathon submission with no evidence of real-world usage.
• Unproven commercial viability: There is no indication of monetization, pricing, or customer demand.
• Limited scope: The tool appears to be a prototype for a specific use case (video analysis and prompt generation) without clear expansion plans.
• Founder-only team: The project was built by one person, which raises questions about scalability and long-term development.

1. What is the current status of the product beyond the hackathon submission?
2. Have you tested this with real users or in real-world workflows?
3. Are there any plans to monetize or scale the tool?
4. How does it compare to existing tools for video analysis or prompt engineering?
5. What are your plans for team expansion or product development?