Quick answer: palette extraction reads the dominant colors from an image and turns them into a brand palette, while an auto-contrast step picks a readable text color over each background. Together they keep the brand kit accurate and the text legible without manual color math.

How palette extraction works

A color analysis step reads the source image and returns a set of representative colors, which become the brand palette. That is faster and more consistent than picking colors by eye, and it keeps the kit tied to an existing logo or visual.

Why auto contrast matters

Text over a brand color needs enough contrast to read, and the correct choice, light or dark, changes with the background. A contrast helper compares the options and returns a readable text color, so the same palette works across light and dark slides.

Where it fits the workflow

  1. Extract the palette from the logo or a reference image.
  2. Set the colors in the brand kit.
  3. Let the contrast step pick the text color over each background.
  4. Check the result on the actual slides.
  5. Reopen the export to confirm readability.

The honest limits

Automation handles the color choice, but it does not replace a review for the brand's intent, and an unusual background may still need a manual check. The extracted palette is only as good as the source image.

Neatico extracts a palette with a color analysis step and applies auto contrast through a dedicated helper, feeding the brand kit. The Frontify alternatives hub compares the tools.

The bottom line

Use palette extraction to build an accurate kit from an image and auto contrast to keep the text readable over the brand colors. Verify the result on the slides, and treat the helper as a way to remove the manual color decisions rather than the review.

How the two features work together

Palette extraction uses a color library to read the dominant colors from an image, producing a coherent set rather than a manual guess. The contrast function then picks a text color that stays readable against any background, including brand colors and photos. Together they keep generated assets on brand while protecting legibility, and they remove the trial-and-error of choosing text colors by hand. The output is deterministic, so the same source image yields the same palette.

Where this saves the most time

The biggest gain is on batches that combine photos and brand colors, where choosing text colors by hand would mean repeated trial and error. Extraction reads the dominant colors deterministically and the contrast function returns a readable text color for each background, so the same source always yields the same result. The two features keep the set coherent and legible without manual sampling, and they work automatically as new assets are generated rather than requiring a separate color pass.