The quiet art of arranging.
Sequencing images the way a curator would — powered by computer vision.
Our story
Cadence was born from a simple observation — arranging images in a visually pleasing sequence is both an art and a time-consuming task. Whether you're building a portfolio, a photo essay, or curating content for social media, the flow between images matters.
We built Cadence to solve this challenge using advanced computer vision and machine learning, automating what might take hours of manual work into a process that takes seconds.
Our goal is to help photographers, designers, social media managers, and anyone working with visual content to create more compelling visual narratives with minimal effort.
How it works
Cadence uses computer vision and machine learning to analyze your images and compose aesthetically pleasing sequences. The algorithm examines multiple visual elements:
Color composition
Dominant colors and palette progression across the set.
Visual texture
Pattern density, light distribution, and surface character.
Edge patterns
Shapes and compositional weight within each frame.
The technology combines K-means clustering with custom feature extraction to identify natural transitions between images. This creates visual flows that would be difficult and time-consuming to arrange manually.
Unlike basic sorting tools that only look at a single dimension like color, our algorithm understands the complex visual relationships between images — finding unexpected connections that create compelling narratives.
Example sequences
See how Cadence creates smooth transitions between various types of images. These small groupings came from an unsorted sequence of 200 images:
These examples show how Cadence identifies relationships between images that might not be immediately obvious — creating harmonious progressions or groupings that guide the viewer's eye naturally. These groups are perfect for cohesive Instagram posts, portfolios, and website layouts.