I Built an AI SEO Brief Generator That Compares 3 Models Side-by-Side — Here's the Architecture

A developer built an AI-powered SEO content brief generator, seobrief.cc, that compares three large language models side-by-side. The tool uses Next.js 16, Supabase, Stripe, and Vercel, and runs DeepSeek, Qwen, and Moonshot in parallel to produce a brief in about 30 seconds.

I spent 3 months building an SEO content brief generator, and I wanted to share the technical architecture with the dev.to community. SEO content briefs are the pre-writing research phase for blog posts — you analyze the SERP, figure out search intent, outline the article structure, and suggest metadata. Most people do this manually, spending 1-2 hours per article. seobrief.cc https://seobrief.cc — keyword in, complete brief out in ~30 seconds. Frontend: Next.js 16 + Tailwind v4 + React Server Components Backend: Next.js API routes edge-compatible Database/Auth: Supabase Postgres + Auth + Row Level Security Payments: Stripe Checkout subscription AI: 3 LLMs running in parallel: Hosting: Vercel production , with @vercel/analytics for traffic typescript // Simplified — the actual route calls 3 providers in parallel const deepseek, qwen, moonshot = await Promise.allSettled generateBrief keyword, 'deepseek' , generateBrief keyword, 'qwen' , generateBrief keyword, 'moonshot' , ; // Score each result, return all 3 + recommendation const results = deepseek, qwen, moonshot .filter r = r.status === 'fulfilled' .map r = { ...r.value, score: scoreBrief r.value } ; return { results, recommended: results.sort a, b = b.score - a.score 0 };