AI Portfolio Assistant: RAG Chat + Job Hunter

AI Integration

•19 de febrero de 2026•4 min read•Por Daily Miranda Pardo

A modern portfolio in 2026 is not a static PDF or a list of screenshots. If you want to monetize your profile, you need a living knowledge base, a chat that answers questions about you in real time, and a system that proactively finds opportunities. That is exactly what AI Portfolio delivers: an intelligent, connected portfolio designed to convert visitors into clients.

This article breaks down the architecture, the stack, and the commercial features that make the project market-ready for freelancers, consultants, and teams who want a portfolio that sells.

The core architecture: a portfolio as a live product

The foundation is built with Next.js 16 (App Router), React 19, and TypeScript, with a clean UI in Tailwind CSS. The key idea is to treat a portfolio like a product, not a brochure.

The system flow looks like this:

Ingestion: CVs, certifications, diplomas, and proprietary documents
Automatic integrations: GitHub + LinkedIn
Unified knowledge base in Supabase with pgvector
Real-time chat using Google Gemini (gemini-2.5-flash)
A proactive Job Hunter that matches your profile to opportunities

Each part reinforces the next. Your data stays fresh, your narrative stays consistent, and every visitor can explore your expertise through a conversational interface.

RAG + streaming chat: real-time sales enablement

The chat experience is built with Vercel AI SDK and streaming responses, which makes the assistant feel immediate and fluid. Instead of generic answers, the model is grounded by your own documents and profile data through RAG.

A simplified streaming endpoint:

import { streamText } from 'ai';
import { google } from '@ai-sdk/google';

export async function POST(req: Request) {
  const { prompt, context } = await req.json();

  const result = await streamText({
    model: google('gemini-2.5-flash'),
    system: 'Answer as a professional portfolio assistant.',
    prompt: `${context}\n\nQuestion: ${prompt}`,
  });

  return result.toTextStreamResponse();
}

Now a recruiter or client can ask:

"What React 19 projects have you shipped?"
"Show me your experience with RAG and vector search."
"How have you used Supabase in production?"

That interaction moves your portfolio from passive to persuasive.

Knowledge base powered by GitHub, LinkedIn, and documents

The real value comes from automation. The system ingests your documents and connects to GitHub and LinkedIn, building a unified knowledge base that stays up to date.

GitHub: repositories, languages, stars, and descriptions
LinkedIn: roles, skills, and education
Personal documents: CVs, certifications, and custom materials

Integration tokens are stored inside profile_data, and sync endpoints keep everything current. The RAG layer does more than search text; it prioritizes the most recent, most relevant sources.

Example semantic query in Supabase:

select * from match_documents(
  query_embedding := $1,
  match_threshold := 0.78,
  match_count := 6
);

That is how the chat can respond with high precision using your own data, not generic internet content.

Multi-user, security, and SaaS readiness

The project runs on Supabase Auth with RLS enabled. Each user gets a private data space, and a trigger automatically creates profile_data on signup. This is essential if you want to monetize at scale.

Why it matters:

Clean data isolation per user
Permission control at the database layer
Secure backend logic with minimal overhead
Ready for freemium or subscription plans

If your goal is a portfolio platform, this foundation is critical.

Job Hunter: from portfolio to active acquisition

The standout commercial feature is the Job Hunter. Instead of waiting for leads, it scans for opportunities, matches them to your profile, and suggests targeted applications. With approval, it can draft cover letters and send them for you.

That transforms the portfolio into a revenue engine:

Automates prospecting
Speeds up response time
Keeps messaging consistent
Reduces the effort of finding clients

From a business perspective, this is a premium add-on, a monthly service, or a B2B package for teams.

Testing and deployment: built for reliability

The project includes Playwright E2E tests for login, dashboard, and OAuth flows. Deployment on Vercel keeps iteration fast and stable, which is essential when productizing a portfolio platform.

To monetize effectively, the platform can bundle analytics, premium templates, and priority lead matching into higher tiers. That gives users a clear upgrade path while keeping the core portfolio free or low cost, and it aligns pricing with measurable outcomes.

If you want to build something similar or integrate this architecture into your product, explore our AI-driven development or AI integration services.

Conclusion

AI Portfolio turns professional experience into a live, conversational product that generates opportunities. It is designed to monetize your profile, keep your knowledge base updated, and help you win clients faster. Need help building a portfolio like this? Contact us.