This is a submission for the Bright Data AI Web Access Hackathon

ZOQ Agent: Universal AI-Powered Outreach & Intelligence System 🎯

What I Built

ZOQ Agent transforms any query into actionable outreach in under 60 seconds. Simply tell it what you want and provide your context - it finds the right people, researches them in real-time, and writes hyper-personalized messages.

Core Capability: Universal query → intelligent outreach pipeline that works for sales, hiring, partnerships, feedback collection, and more.

Examples in Action:

🚀 Sales: "Find AI startup founders in Bangalore" → Discovers prospects → Writes personalized cold emails mentioning their recent funding/product launches
💼 Hiring: "Find senior React developers at YC companies" → Identifies candidates → Crafts recruitment messages referencing their specific projects
🤝 Partnerships: "Find CTOs building developer tools" → Locates decision makers → Creates collaboration proposals based on their tech stack
📊 Feedback: "Find AI agent product users" → Discovers early adopters → Writes feedback requests mentioning their specific use cases

Demo

🔗 Live Demo | 🔗 GitHub Repository | 🎥 Demo Video

Real Execution Flow:

Query: "Find AI startup founders who raised funding in 2024 - pitch our sales automation tool"
Discovery: Searches across multiple sources, finds 3 matching founders
Enrichment: Scrapes LinkedIn profiles, company websites, recent news about their funding
Email Generation: Creates personalized emails like:

Hey [Name],

Congrats on the $2M Series A for [Company]! Saw your interview about scaling challenges.

At ZOQ, we automate the entire sales pipeline - exactly what you need while scaling from 5 to 50 customers.

We helped [Similar Company] book $50K pipeline in 6 weeks, all automated.

Worth a 15-min chat about handling your sales while you focus on product?

Best,
[User]

Performance: 3 hyper-personalized emails generated in 45 seconds with 90%+ personalization accuracy.

How I Used Bright Data's Infrastructure

ZOQ Agent leverages ALL 4 Bright Data MCP capabilities in a sophisticated multi-agent orchestration:

🔍 Discover: Multi-Source Intelligence Gathering

Tool: search_engine via Bright Data MCP
Usage: Executes 8-10 parallel searches per query to find prospects across Google, LinkedIn, company databases
Smart Queries: AI generates diverse search strategies (location-based, role-based, company-based, industry-based)

🌐 Access: Complex Site Navigation

Tools: web_data_linkedin_person_profile, web_data_linkedin_company_profile
Challenge Solved: Accesses LinkedIn profiles and company pages that require authentication and complex navigation
Result: Successfully extracts profile data from protected professional networks

📊 Extract: Structured Real-Time Data

Tools: scrape_as_markdown for company websites, search_engine for news/funding data
Data Extracted: Recent activities, funding rounds, product launches, team changes, pain points, tech stack
Structure: AI converts raw web data into structured JSON with email signals, personalization hooks, and opportunity insights

⚡ Interact: Dynamic Content Handling

Implicit Usage: Bright Data tools handle JavaScript-heavy sites, dynamic loading, and anti-bot measures
Sites Handled: LinkedIn (complex auth), modern company websites (SPA frameworks), news sites (dynamic content)
Result: Reliable data extraction from modern web applications that traditional scraping can't handle

Multi-Agent Architecture:

User Query → Planning Agent → Discovery Agent (search_engine)
→ Enrichment Agent (LinkedIn + website scraping)
→ Email Writing Agent → Personalized Results

Agent Action Panel

Prospect Card with personalized email

Each agent uses Bright Data tools intelligently based on AI decision-making, creating a fully autonomous research and outreach system.

Performance Improvements

Real-time web data access transforms AI performance from generic to hyper-personalized:

Before Bright Data (Traditional AI):

❌ Generic Email Template:

"Hi [Name], I hope this email finds you well.
I wanted to reach out about our product..."
- **Personalization**: 20% (name/company only)
- **Response Rate**: ~2-3%
- **Research Time**: Manual, hours per prospect
- **Data Freshness**: Outdated, static information

After Bright Data (ZOQ Agent):

✅ Hyper-Personalized Email:

"Hey Alex, Congrats on Buildspace S5 Demo Day last week!
DashChat's text-to-SQL interface is brilliant.

Building + validating + finding users solo is brutal - I've been there.

At ZOQ, we automate your entire user acquisition while you iterate on product.
We helped techcorp book $90K pipeline in 6 weeks.

Worth a chat about getting you 50+ beta testers this month?"

Measurable Improvements:

Personalization Accuracy: 90%+ (mentions specific recent activities)
Research Speed: 45 seconds vs 2+ hours manual research
Data Freshness: Real-time (funding announcements, product launches, recent posts)
Contextual Relevance: AI references specific pain points, recent events, company news
Scalability: 100+ prospects/day vs 5-10 manual research

Real-Time Data Advantage Examples:

Funding News: "Congrats on your $2M Series A announced yesterday"
Product Launches: "Saw your ProductHunt launch hit #3 this week"
Team Updates: "Noticed you're hiring 5 engineers - scaling fast!"
Industry Events: "Great talk at TechCrunch Disrupt on AI automation"

ROI Impact:

Time Savings: 95% reduction in research time (2 hours → 45 seconds)
Response Rate: 3x improvement with personalized hooks
Pipeline Quality: Higher conversion due to relevant, timely outreach
Scalability: 20x more prospects reached with same effort

The Key Differentiator: Traditional AI relies on static training data, but ZOQ Agent accesses fresh, contextual information that makes prospects think "they actually researched me" instead of "this is clearly automated."

Bright Data's reliable, real-time web access is what transforms generic AI into intelligent, contextual automation that drives real business results.

Give the Bright Data MCP repo some love! 🌟

Tamizh @tamizhme