How I Built an AI-Powered Reverse Hiring Platform with Next.js, Supabase, and OpenAI

A developer built an AI-powered reverse hiring platform using Next.js, Supabase, and OpenAI. The platform flips the traditional hiring model by requiring companies to find candidates, disclosing salary upfront, and keeping candidates anonymous until they engage. The system uses OpenAI's GPT-4o-mini for resume parsing and match scoring, with embeddings and cosine similarity to determine compatibility.

How I Built an AI-Powered Reverse Hiring Platform with Next.js, Supabase, and OpenAI https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fiikm8lf9ycuz7nb85fll.png The hiring process is fundamentally broken. Candidates send hundreds of applications into a void of automated rejections. Companies pay €25,000 per hire to recruiting agencies who copy-paste LinkedIn profiles into emails. Everyone works harder, nobody gets better results. So I asked: what if we flipped the entire model? What if companies had to find candidates — not the other way around? What if salary was disclosed before the first conversation? What if candidates stayed anonymous until they chose to engage? That's what I built. Here's how. | Layer | Technology | |---|---| | Framework | Next.js 15 App Router | | Database + Auth | Supabase PostgreSQL + RLS + Auth | | AI | OpenAI GPT-4o-mini + text-embedding-3-small | | Hosting | Vercel serverless | | Resend | | | PDF Parsing | unpdf WASM-based, works serverless | | Styling | Tailwind CSS + Framer Motion | | State | Zustand | This is the most interesting part technically. Here's how match scoring works: js export async function parseResume text: string { const response = await openai.chat.completions.create { model: 'gpt-4o-mini', temperature: 0.2, response format: { type: 'json object' }, messages: { role: 'system', content: You extract structured profile data from resumes. Return JSON with: headline actual job title from resume, max 80 chars , bio 2-3 sentence summary preserving substance, anonymous , skills array, max 15 , experience years integer , industry, location. Important: Do NOT genericize. A "Senior Engineering Manager" should NOT become "Software Engineer". }, { role: 'user', content: text } , } return JSON.parse response.choices 0 .message.content || '{}' } Key lesson: the prompt must explicitly say "do not genericize." Without that instruction, GPT-4o-mini tends to normalize every title to "Software Engineer" and writes generic bios. export async function generateEmbedding text: string : Promise<number { const response = await openai.embeddings.create { model: 'text-embedding-3-small', input: text, } return response.data 0 .embedding } export function cosineSimilarity a: number , b: number : number { let dot = 0, magA = 0, magB = 0 for let i = 0; i < a.length; i++ { dot += a i b i magA += a i a i magB += b i b i } return dot / Math.sqrt magA Math.sqrt magB } The candidate's profile text and the company's hiring needs are both converted to embeddings, then compared via cosine similarity. A 60%+ score is a "Good Match" in embedding space it rarely goes above 90% even for near-identical texts . After getting the similarity score, GPT generates 3-5 short reasons why they match: js const response = await openai.chat.completions.create { model: 'gpt-4o-mini', messages: { role: 'system', content: Given a candidate and company, return JSON with "reasons": array of 3-5 short reasons max 8 words each why they match. }, { role: 'user', content: Candidate: ${candidateProfile}\nCompany: ${companyNeeds}\nScore: ${score}% } , } This gives employers actionable context: "Skills aligned", "Salary fit", "Remote compatible" — not just a number. This was my biggest headache. pdf-parse the standard Node.js PDF library does not work on Vercel serverless . It depends on a test file that Vercel strips during deployment. What happens: the PDF buffer arrives fine, but pdf-parse silently fails. The fallback reads raw binary as text, sends garbled data to GPT, and GPT hallucinates a completely made-up profile. The fix: I switched to unpdf — a WASM-based PDF text extraction library specifically designed for serverless environments: js import { extractText } from 'unpdf' const buffer = Buffer.from await file.arrayBuffer const { text } = await extractText new Uint8Array buffer const extracted = Array.isArray text ? text.join '\n' : text Works perfectly on Vercel. Lesson: always test your PDF parsing on the actual deployment target, not just locally. Supabase RLS is powerful but tricky with multi-role apps. My setup: SECURITY DEFINER function: bash CREATE OR REPLACE FUNCTION is admin RETURNS boolean AS $$ SELECT EXISTS SELECT 1 FROM profiles WHERE user id = auth.uid AND role = 'admin' ; $$ LANGUAGE sql SECURITY DEFINER; CREATE POLICY "Admins can read all profiles" ON profiles FOR SELECT USING auth.uid = user id OR is admin ; The SECURITY DEFINER prevents infinite recursion — without it, the policy checks itself while checking itself. The AI estimates salary based on candidate's location: content: You are a compensation analyst. Given a professional profile, estimate their market value based on their location. Return JSON with: estimated salary integer, annual in local currency , currency 3-letter code , demand score 0-100 , reasoning one sentence . Adjust for country/city cost of living and local market rates. A senior engineer in San Francisco earns more than one in Helsinki. The currency is then mapped on the frontend based on their country selection — so US candidates see "$150,000" and Finnish candidates see "€130,000". The single most impactful feature for engagement: email notifications via Resend. Every meaningful action triggers an email: Without these, users sign up and forget. With them, they come back. If you're a developer tired of the application grind: jobnex.io https://jobnex.io — free during beta, 2 minutes to set up. If you're hiring: browse the talent pool directly. Happy to answer questions about the technical implementation in the comments. Tags: nextjs ai startup webdev supabase