fylm.ai is a cloud-based AI color grading platform for creating LUTs and editing images with tools like NeuralToneAI and NeuralFilmAI.
The platform processes raw files from DSLRs, mirrorless cameras, and smartphones using ACEScct color management for consistent workflows. NeuralToneAI applies AI models to generate grades, match shots, and auto-correct colors based on image context. NeuralFilmAI delivers neural-network film emulations, exporting as LUTs, XMP profiles, or Capture One styles. Subtractive CMY model simulates film emulsion processing, increasing saturation in darker tones via a density slider.
Primary controls include wheels and bars with GPU acceleration for instant adjustments. Curves support 4096-point precision in 32-bit processing to maintain highlight and shadow detail. Secondaries offer hue, saturation, and luminance controls per color range, plus luminance vs. saturation and saturation vs. saturation qualifiers. Scopes display RGB parades, waveforms, vectorscopes, and histograms. Blending modes such as overlay, soft light, and darken enable effects like bleach bypass.
Asset management centralizes files with instant search and filtering. Collaboration supports private team access, comments, and simultaneous edits. Magic Mode provides a step-by-step LUT builder completing in 18 seconds. Version 2.2 adds tetrahedral interpolation for smoother AI interpolations.
Competitors include color.io for web-based grading with basic AI matching, and Colourlab.ai for automated shot balancing in ACES space. fylm.ai offers broader browser collaboration and film-specific AI, with plans from free evaluation to team tiers adding users and storage, positioned as more affordable for remote workflows than desktop alternatives.
Users report up to 10x faster on-set LUT creation and 90% time savings on show setups. Limitations involve internet dependency and free tier caps at 1GB storage and 2048px exports. AI handles 85% of primaries effectively but requires manual refinement for creative specifics.
Test NeuralToneAI on a single frame to evaluate matching accuracy before scaling to projects.