Build an AI-Powered Design Feedback Tool
Create an intelligent design review platform that analyzes UI/UX screenshots and mockups, providing automated feedback on layout, accessibility, consistency, and visual hierarchy using vision-capable LLMs.
AI Backbone & Vision Analysis
Core LLM infrastructure for analyzing design screenshots and generating structured feedback using vision-capable models
Vercel AI SDK provides unified interface to vision-capable models (GPT-4o, Claude) with structured output for extracting design issues, scores, and recommendations from uploaded screenshots
AI gateway that routes to 100+ LLM providers, useful if you need flexible model switching between vision models or want to self-host the routing layer
Run open-source vision models locally for cost-sensitive or privacy-critical design review pipelines without sending designs to external APIs
Design Capture & Processing
Automated screenshot capture and visual asset processing pipeline to feed designs into the AI analysis layer
Automates browser interactions to capture live website screenshots, navigate design prototypes, and extract visual states for AI analysis
Crawls and captures full-page screenshots from live URLs, useful for batch-processing multiple pages of a design system for consistency review
LLM-friendly web crawler that can extract visual and structural data from live sites, feeding design context into the feedback pipeline
Structured Feedback & Memory
Organize design feedback into structured data, track design decisions over time, and maintain context across review sessions
Extracts structured design feedback (severity, category, location, suggestion) from LLM outputs using Pydantic schemas, ensuring consistent and actionable review reports
Maintains memory of past design reviews, brand guidelines, and team preferences so feedback becomes more contextual and consistent over repeated sessions
Vector database for storing and retrieving design patterns, past feedback, and brand guidelines to provide context-aware suggestions
Agent Orchestration & Workflow
Coordinate multiple specialized review agents (layout, color, accessibility, typography) into a unified feedback pipeline
Builds a multi-agent graph where specialized reviewers (accessibility agent, color theory agent, layout agent) run in parallel and merge into a unified design report
Orchestrates role-playing AI agents — assign a UX reviewer, accessibility auditor, and brand consistency checker as distinct crew members collaborating on design feedback
Multi-agent framework enabling conversational design critique where agents debate and refine feedback before presenting final recommendations
Interactive Frontend & Reporting
User-facing interface for uploading designs, viewing annotated feedback, and collaborating on design improvements
Builds a conversational UI where designers upload mockups, receive AI annotations overlaid on their designs, and can ask follow-up questions about specific feedback points
Provides a polished chat interface with image upload support, letting designers interact with the feedback system through a familiar ChatGPT-style UI
Embeds the AI design reviewer directly into existing React applications as a copilot sidebar, enabling in-context feedback without leaving the design tool