Design Buddy is an AI design reviewer that catches issues before you share your work layout, typography, color, contrast, accessibility, hierarchy, and spacing. Each gets a score with specific, actionable design feedback. Use it as your pre-flight AI design
Product Demo Video
Design Buddy is an AI design review and feedback tool that analyzes visual designs across five structured evaluation dimensionsOverall impression, Layout, Colors, Typography, and Accessibilityand delivers specific, actionable scores and written feedback in the format that a senior designer would provide during a professional design review.
The platform integrates directly with Figma, Canva, and Adobe Express as a plugin, bringing AI design critique into the tools where designers are already working rather than requiring design files to be exported, uploaded to a separate review platform, or reformatted for external analysis.
This in-workflow integration makes design review a natural step in the design process rather than an administrative step that competes with design time.
The five-category review structure provides the comprehensive, balanced design assessment that most designers require but rarely have consistent access to. The Layout evaluation examines visual hierarchy, spacing, alignment, and compositional balance.
The Colors review assesses palette harmony, contrast relationships, and emotional appropriateness for the design's purpose. Typography feedback covers font pairing quality, type size hierarchy, line length, and readability.
The Accessibility review applies WCAG (Web Content Accessibility Guidelines) standards to identify potential barriers for users with visual impairments or other accessibility needs.
The Overall score synthesizes these four dimensions into a single quality indicator that gives designers a quick gauge of their design's aggregate readiness.
The accessibility evaluation is particularly noteworthy for its technical precision. Design Buddy calculates color contrast ratios using the standard WCAG mathematical formula rather than estimating accessibility through AI inference, which would introduce approximation error.
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