
Fylm.ai
Speed up color grading with AI-powered precision

What is Fylm.ai?
fylm.ai is a cutting-edge cloud-based tool designed to simplify the color grading process for both professionals and beginners in photography, videography, and post-production. With the power of AI and intuitive design, fylm.ai enables users to enhance images and video with cinematic color tones, accurate LUTs, and consistent grading across projects — all directly in the browser. Target audience: Filmmakers, video editors, photographers, content creators, colorists, and creative teams looking to optimize their grading workflows without high-end hardware or complex software. Core functionality: fylm.ai uses neural networks and advanced AI models to analyze footage or raw photos and automatically apply film-like color grades. With NeuralToneAI, the platform can deliver natural, studio-quality results in just a few clicks. Users can also create or fine-tune LUTs (Look-Up Tables), match clips, and collaborate with teammates remotely. Key features: AI-powered color grading, LUT generation and matching, support for RAW and video files, cloud-based collaboration tools, color space conversion, visual grading previews, and integration with existing editing pipelines via LUT export. fylm.ai also supports HDR workflows and non-destructive grading. Benefits and advantages: fylm.ai significantly speeds up color grading by removing guesswork. The AI engine produces professional color tones that mimic popular cinematic styles, making it a game-changer for small teams and solo creators. It eliminates the need for deep technical knowledge while still offering advanced features for expert users. Because it’s cloud-based, collaboration is seamless—clients or teammates can view and comment on grades remotely. The result: beautiful, consistent visual output with minimal effort. Integrations: fylm.ai works entirely online and exports LUTs compatible with DaVinci Resolve, Adobe Premiere Pro, Final Cut Pro, and other industry-standard tools. Its platform-agnostic approach makes it easy to fit into any existing post-production workflow.