Why it matters
Traditional AI generation relies entirely on text prompts. You have to describe every detail in words — color palette, lighting style, camera angle, mood. This is slow and imprecise, especially for visual creators who think in images. The vision engine lets you show instead of tell. Upload a reference image with the style you want, and the analysis gives the AI agent the context it needs to generate matching content.Image analysis
When the vision engine analyzes an image, it extracts:- Style — Artistic style, rendering technique, visual approach
- Composition — Layout, focal points, balance, visual hierarchy
- Color palette — Dominant colors, color temperature, contrast levels
- Camera language — Angle, distance, depth of field, perspective
- Mood and atmosphere — Emotional tone, lighting quality, environmental context
Video analysis
For videos, the vision engine adds motion-specific analysis:- Motion patterns — Camera movement, subject movement, speed
- Pacing — Cut frequency, rhythm, temporal flow
- Lighting transitions — How lighting changes across the video
- Narrative structure — Scene progression, visual storytelling elements
Using analysis in your workflow
Analysis results help in two ways:- Inform generation — The AI agent uses analysis data to generate content that matches your references. When you say “Generate an image in this style”, the agent knows exactly what “this style” means because the vision engine has already broken it down.
- Creative feedback — Review the analysis to understand what makes a reference image or video work. The breakdown of composition, color, and camera choices can guide your creative decisions.