The research, personas and flows behind Crop.photo: AI Redesign. The case study covers the decisions; this is the work underneath them.
Three distinct user types driving demand for AI-powered image editing. Each with unique workflows, constraints, and expectations from a tool like Crop.photo.
78% of e-commerce sellers still crop and retouch product images manually. A massive unaddressed opportunity for AI automation at scale.
The average content creator wastes 45 minutes per day on image resizing and formatting tasks, nearly 4 hours of recoverable productive time per week.
AI-powered image editing tools reduce total editing time by up to 85% compared to manual workflows, validating the core product thesis and market timing.
Tools and methods used throughout the design process, from initial research and wireframing through to final prototype and handoff.
A five-phase process covering both the marketing homepage and the application interface. Grounded in e-commerce user research, competitive analysis, and conversion-optimised visual storytelling.
Audited crop.photo against Remove.bg, Canva, and Clipping Magic. Studying homepage value communication, feature discoverability, and batch processing UX patterns. Mapped crop.photo's 8 AI features against how they surfaced in the existing interface.
Deliverables: Competitive matrix, feature audit, UX gap analysis.
Mapped three primary user archetypes, e-commerce sellers (bulk crop & background removal), marketing designers (multi-platform export), and fashion brands (headless model shots). Identified the marketplace export friction as the highest-priority UX failure point.
Deliverables: Persona cards, pain point prioritisation matrix.
Designed a before/after AI slider as the homepage hero, replacing the existing feature list with a live demonstration. A single drag communicates AI bulk cropping capability without any reading, targeting a 3-second value comprehension window.
Deliverables: Hero interaction model, demo component, CTA hierarchy.
Redesigned the core application screens: unified batch dashboard, marketplace-aware preset selector, AI confidence score per image, and dedicated entry points for Headless Face Cropper, Listing Analyzer, and AI Fashion Model Generator.
Deliverables: App wireframes, feature flow maps, interaction specs.
Produced pixel-perfect screens in Figma, homepage, batch dashboard, preset selector, and batch preview grid, with complete design system tokens, brand-accurate blue palette, and a component library covering all 8 AI feature surfaces. Crop.photo's users include professional e-commerce teams operating under accessibility requirements for their own storefronts. All components were built with WCAG AA as a baseline. Keyboard navigation paths were mapped for the batch upload flow, a particularly complex interaction, ensuring the tool is usable without pointer interaction.
Deliverables: Hi-fi prototype, component library, style guide, accessibility checklist, Behance publish.