Building an Unstoppable AI Interview Coach: How Real-Time Bright Data + n8n Workflows Changed Everything

This is a submission for the AI Agents Challenge powered by n8n and Bright Data

What I Built

HireWise is an AI-powered interview preparation platform that solves a critical problem: outdated interview preparation. While most interview prep tools rely on static, generic questions, HireWise combines real-time job market intelligence with lifelike AI interviews to give candidates a genuine competitive edge.

The Problem: Traditional interview prep uses yesterday's questions for today's job market.

The Solution: HireWise scrapes current LinkedIn job postings using Bright Data, processes them through n8n AI workflows, and generates market-relevant interview questions delivered by realistic AI avatars.

Key Features:

• 🔍 Real-Time Market Intelligence: Live LinkedIn job scraping for current interview questions
• 🤖 Lifelike AI Interviews: Voice-based conversations with Akool streaming avatars
• 📝 Adaptive Question Generation: Personalized based on resume uploads or job descriptions
• 💡 Intelligent Feedback: AI-powered performance analysis and improvement suggestions
• 🔐 Secure Platform: Clerk authentication with interview history tracking

Demo

🎥 YouTube: https://youtu.be/Kx_ednL1EUo

🌐 Live Application: https://hirewise-delta.vercel.app/

User Flow:

1. Upload Resume or enter job details manually
2. AI processes your information through n8n workflows
3. Bright Data scrapes current LinkedIn job postings
4. Interview begins with lifelike AI avatar
5. Voice conversation with real-time questions
6. Receive feedback and performance insights

n8n Workflow

📋 GitHub Gist: HireWise n8n Workflows

🖼️ Journey With n8n and BrightData*:

The workflow demonstrates the "unstoppable workflow" concept through:

• Intelligent Branching: Different paths for resume vs. manual input
• Multi-AI Processing: OpenAI + Google Gemini integration
• Real-Time Data: Bright Data LinkedIn scraping
• Dynamic Response: Contextual question generation

Technical Implementation

Architecture Overview

• Frontend: Next.js 15 with React 19, TypeScript, Tailwind CSS
• Backend: Next.js API routes with Convex database
• AI Integration: Multiple AI models for different tasks
• Authentication: Clerk for secure user management
• Rate Limiting: Arcjet for API protection

System Instructions & Model Choices

Question Generation (OpenAI):

Generate 5 relevant interview questions based on:
1. Current job market data from LinkedIn
2. User's resume/job description
3. Industry-specific requirements
4. Behavioral and technical aspects

Resume Processing (Google Gemini):

Extract and structure resume content:
- Skills and experience
- Education background
- Project highlights
- Professional summary

Feedback Analysis (OpenAI):

Analyze interview conversation and provide:
- Performance rating (1-10)
- Specific feedback on responses
- Improvement suggestions
- Industry benchmarking

Memory & State Management

• Convex Database: Persistent interview sessions and user history
• Session Storage: Real-time conversation tracking
• Workflow State: n8n handles complex branching logic

Tools Used

• Akool Streaming Avatar SDK: Lifelike AI interviews
• ImageKit: Secure PDF resume storage
• Clerk: Authentication and user management
• Arcjet: Rate limiting and security

Bright Data Verified Node

The Bright Data integration is the heart of our "unstoppable workflow":

How We Used Bright Data:

1. Dynamic LinkedIn Scraping: Based on user-provided job titles
2. Two Workflow Paths:
   • Path 1: Resume-based job matching
   • Path 2: Manual job description processing
3. Real-Time Market Data: Current job postings, requirements, trends
4. Data Processing: Custom n8n code nodes clean HTML and extract relevant content

Configuration:

{
  "parameters": {
    "url": "https://www.linkedin.com/jobs/search/?keywords={{ encodeURIComponent($json.jobTitle) }}",
    "zone": "web_unlocker1",
    "country": "US"
  }
}

Value Added:

• Market Relevance: Questions reflect what employers are actually asking
• Current Trends: Incorporates latest industry requirements
• Competitive Edge: Users practice with today's market expectations

The scraped data flows through our n8n AI agents, which analyze job descriptions, extract key skills, and generate targeted interview questions that mirror real hiring manager expectations.

Journey

Initial Challenge

The biggest challenge was creating a system that could provide truly relevant interview preparation. Generic questions don't prepare candidates for the specific requirements and trends in today's fast-moving job market.

The n8n + Bright Data Breakthrough

Discovering the synergy between n8n's AI Agent capabilities and Bright Data's web scraping opened up possibilities I hadn't considered:

1. Real-Time Relevance: Instead of static question databases, we could scrape current job postings
2. Intelligent Processing: n8n's AI agents could analyze and contextualize scraped data
3. Personalization: Combining market data with user resumes created truly personalized experiences

Technical Hurdles Overcome

Complex Workflow Orchestration: Building branching logic in n8n that handled both resume-based and manual flows required careful planning and testing.

Data Processing: LinkedIn's HTML needed extensive cleaning and processing before it could be useful for question generation.

AI Synchronization: Coordinating multiple AI models (OpenAI, Gemini, Akool) while maintaining conversation flow and context.

Real-Time Performance: Ensuring the voice-based AI interviews felt natural and responsive.

What I Learned

1. n8n's Power: The platform's ability to orchestrate complex, multi-step AI workflows is incredible
2. Data Quality Matters: Raw scraped data needs significant processing to be useful
3. User Experience: Real-time market data only matters if it enhances the user experience
4. Workflow Design: Thinking in terms of "unstoppable workflows" changed how I approach automation

Unexpected Discoveries

• Bright Data's reliability made real-time scraping feasible for production use
• n8n's AI agents could handle much more complex logic than expected
• The combination created emergent capabilities neither tool provided alone

Results

HireWise now provides interview preparation that:

• ✅ Stays current with job market trends
• ✅ Personalizes to individual user backgrounds
• ✅ Offers realistic interview simulation
• ✅ Provides actionable feedback for improvement

The platform demonstrates how combining AI agents with real-time web data creates "unstoppable workflows" that adapt and improve based on current market conditions.

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Live Demo: https://hirewise-delta.vercel.app/

Source Code: https://github.com/Arjunhg/hirewise

Workflows: GitHub Gist

Built with ❤️ using n8n AI Agents and Bright Data

Comments

[Anik Sikder]

This is one of those builds that makes you pause and go, ‘Wait, why isn’t this already the norm?’ The real-time scraping + lifelike AI interviews + adaptive feedback loop is such a powerful combo. Loved the clarity in your architecture and the way you handled branching logic with n8n. Curious how you’re managing edge cases like vague resumes or job descriptions with buzzwords but no substance. Either way, this feels like a huge leap forward in interview prep.

[Arjun Sharma]

Hey, thanks for testing out the app! 😊About your question, the automation tries to pull out the top 5 keywords and job title from the resume. But let's say resume’s empty (which is more like testing the limits 😅) or it's not able to parse it, so it just defaults to 'Software Engineer' and generic stuff like aws, development, etc.
Definitely a bit limited in that edge case, but tbh, most people won't be uploading blank resumes or typing in random stuff unless they’re purposely trying to break things (which is fair for testing).
This is just the first prototype though, still lots to build, improve, and figure out. But I really appreciate the feedback! These convos help a ton in shaping the next version 🙌