ReTake

🎬 Inspiration

A screenplay tells you what happens. It doesn’t tell you what will go wrong when you try to shoot it.

For independent filmmakers, short-film creators, and small studios, pre-production is a logistical nightmare. Wardrobe continuity drifts across non-linear shooting schedules. Actor availability collides with sunset windows. Unnecessary location switches burn hours of daylight. Often, these mistakes are only discovered on set, when every wasted hour costs hundreds or thousands of dollars.

We asked ourselves: "What if an AI could read the script, look at our constraints, and stress-test the production before we spend a single dollar?"

That's why we built RETAKE.

💡 What it does

RETAKE acts as your elite, autonomous production supervisor. You simply input your screenplay and your production constraints (e.g., "Sarah is only available Day 2," "We only have the apartment for one day"). RETAKE then deploys a network of seven specialized AI agents to analyze the shoot. It detects hidden continuity risks, clusters locations to save time, cross-references real-world data (like sunset times and weather), and finally simulates the shoot.

It compares your current shooting plan against an AI-optimized plan, showing you exactly how many hours of production time you can save, and outputs a complete, day-by-day Production Blueprint.

⚙️ How we built it (The Multi-Agent Architecture)

We built a deterministic, multi-step agent workflow powered by Gemini 2.0 Flash and grounded entirely by Parallel Search.

The app features a luxury, cinematic frontend built with React, Vite, Tailwind CSS, and Framer Motion (complete with a 3D clapperboard and film grain). But the real magic happens in our Node/Serverless backend, which orchestrates a sequential 7-agent pipeline:

Script Supervisor: Extracts scenes, locations, props, and wardrobe. Continuity Detective: Hunts for story-state inconsistencies and wardrobe drift. Logistics Engine: Clusters location movements. Schedule Engine: Aligns actor availability constraints. Location Scout (Powered by Parallel): See below. Risk Detective: Adversarially asks, "How could this fail?" Production Supervisor: Synthesizes the final blueprint and Health Score.

🔍 The Parallel Search Integration

To dominate the Parallel Track, we knew our agents couldn't rely on hallucinated data. Our Location Scout Agent actively uses the @anthropic-ai/parallel-web SDK at runtime.

When the Script Supervisor extracts locations (e.g., "Rooftop, Sunset"), the Location Scout calls the Parallel Search API to pull live, real-world grounding data:

Exact sunset and golden-hour times for the specified location. Current weather forecasts to flag outdoor shooting risks. Local filming permit requirements. This real-time web intelligence is fed directly into the Schedule Engine to ensure the AI's optimized shooting plan works in the real world, not just on paper.

🚧 Challenges we ran into

Orchestrating seven distinct LLM calls sequentially while maintaining speed and data structure was difficult. If Agent 2 hallucinated, Agent 7 would output garbage.

We solved this by using Gemini's strict response Schema (Structured Outputs) for every single agent call. We also had to ensure that if the Parallel Search API encountered a rate limit or network error, the orchestration pipeline could degrade gracefully without breaking the entire analysis.

🏆 Accomplishments that we're proud of

The Parallel Grounding: Seeing the AI adjust a shooting schedule because Parallel Search informed it that sunset is at 6:47 PM is a true "magic" moment. The UX/UI: We successfully built a "Hollywood-grade" interface. The Framer Motion animations, the 3D clapperboard, and the dark cinematic styling make enterprise AI feel like a blockbuster tool. The "Simulate the Shoot" feature: Proving ROI to filmmakers by showing them Current Plan (12 hrs) vs Optimized Plan (9 hrs) makes the AI's value instantly quantifiable.

📚 What we learned

We learned the sheer power of Agentic Roles. Simply asking an LLM to "analyze this script for production" yields generic advice. But creating specialized, deterministic agents—where a "Continuity Detective" has a different system prompt and adversarial goal than a "Schedule Engine"—produces shockingly professional, industry-accurate intelligence.

🚀 What's next for RETAKE

PDF File Parsing: Allowing users to directly upload Final Draft (.fdx) and PDF screenplays. Budget Engine: Adding an 8th agent that cross-references union rates and equipment rental costs to generate a preliminary budget. Call Sheet Generation: Automatically generating industry-standard, shareable PDF call sheets for the cast and crew based on the optimized blueprint.

Built With

• framer-motion
• gemini
• github
• google-ai-studio
• parallel
• parallel-search-api
• react
• tailwind-css
• typescript
• vercel
• vite