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:

1. Query: "Find AI startup founders who raised funding in 2024 - pitch our sales automation tool"
2. Discovery: Searches across multiple sources, finds 3 matching founders
3. Enrichment: Scrapes LinkedIn profiles, company websites, recent news about their funding
4. 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.

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Give the Bright Data MCP repo some love! 🌟

Comments

[Subash chandra Bose A]

awesome!, its actually searching for people and getting their contact info. and the email is also personalized. great work. what llm are you using, is it cheap??

[Tamizh]

Thank!, glad to know it worked for you. Since its a multi agent system, I used mix of models like claude sonnet 3.7 for synthesis, gpt 4.1 mini/deepseek v3 for research, gpt 4.1 nano for master agent via openrouter.

[Ranvijay Singh]

ZOQ Agent isn’t just an AI… it’s a weapon. Clean. Ruthless. One prompt and boom a full-blown outreach strike. If I ran tech, this would be my go-to.
Respect to the creator… you didn’t build a tool, you unleashed a beast.

[Tamizh]

This feels like a GPT message, but seriously, thanks!.

[Sonal Noel Raj]

This is wild. The way this agent autogenerates with real-time data (like funding rounds or recent launches) makes it feel truly aware, not just automated. How hard was it to sync all four Bright Data tools into one flow? curious, is the mcp you mentioned is the key?? im not understanding it well

[Tamizh]

Thanks! Honestly, it’s surprisingly smooth once you get the hang of it. Bright Data’s MCP acts like a plug-and-play toolset, you just run the MCP server, and your LLM can directly use any of the 50+ built-in tools without having to manually wire up API calls or handle responses.

Since my setup is multi-agent, each agent taps into different tools (search, LinkedIn scraping, dynamic site handling, etc.), and MCP handles all the heavy lifting — auth, retries, JavaScript rendering, the works. Makes orchestration way easier. You just run the mcp server(npx @brightdata/mcp) and bind the tools with your LLM call.

Definitely check out the repo README or Bright Data’s docs — it’ll click once you see it in action!

[Rooney The Retriever]

Impressive work on ZOQ Agent! The ability to transform a simple query into a fully personalized outreach sequence in under a minute is a game-changer. The integration of Bright Data’s multi-agent orchestration showcases a deep understanding of modern AI capabilities.

[Tamizh]

Thanks, all thanks to the mcp part, that brings down the dev time significantly. Imagine writing API call functions for each tools.

[Drake Ramoray]

ZOQ Agent writes smarter than I act and that’s saying something!

Seriously, this is next-level AI magic.

[Tamizh]

Thanks!

[Noah Kalson]

Please update your links.

[Tamizh]

Hi Noah, got into deployment issues, now it should be working.

[Cyber Sniper]

This is an Awesome project, Thanks for sharing, very useful, maybe we can collaborate on my Project?

[Tamizh]

Hi, Thanks for the comment. We can talk about your project.

[Bhagat Surya]

honestly really good. feels like this could replace 3 tools i’m using rn lol. all the best!

[Tamizh]

Thanks, yeah, I thought of it like, why not perplexity for people!

[Andre]

I've used parallel tools before and found that content generation often hits the mark about 80% of the time. Did you do any grading on whether you would personally be happy to send these out? A human review step already built in 👏

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