DEV Community: Techforce Global

How Automated YC Founder Data Collection Gives Sales, Recruiting & VC Teams a Real Edge

Techforce Global — Wed, 08 Apr 2026 12:44:47 +0000

The Problem Nobody Talks About

You open Y Combinator Startup School. You find a founder. You copy their name, bio, location. Then you open LinkedIn separately, search for the same person, scroll through five profiles with similar names, pick the one that looks right and paste the URL into your spreadsheet.

Then you do it again. For the next founder. And the next.

At 10 profiles, that's a couple of hours. At 100, it's a week you don't have. At 500, it simply doesn't happen and your team works from incomplete data or skips the research entirely.

That's the problem the Y Combinator Founder Scraper by Techforce Global solves. It extracts founder profiles from YC Startup School and automatically matches each one to their LinkedIn URL using intelligent matching algorithms, not guesswork. One run. Clean, structured, enriched data. Ready to use.

"A name alone is a phone book entry. A matched, enriched founder profile is a working lead."

What Automated YC Founder Data Actually Gives Your Team

In plain terms, automation turns scattered founder information into a working dataset. Every record includes:

Founder name and short bio
Location (city and country)
Education history university, degree, graduation year
Employment history previous companies and roles
YC Startup School profile URL
Automatically matched LinkedIn profile URL
What the founder is looking for in a co-founder

That last field what they're looking for is unique to YC Startup School and often overlooked. It tells you a lot about where the founder is in their journey and what kind of conversation they're open to. For sales, recruiting, and VC teams, that context changes the quality of the first outreach.

Raw List vs. Usable Founder Intelligence

The difference between a raw name list and an enriched founder dataset isn't just volume it's what you can actually do with the data.

That extra context changes the value of the data entirely. A name alone tells you someone exists. An enriched profile tells you who they are, where they've been, and how to reach them on the right platform.

How Each Team Turns This Data Into an Advantage

The edge isn't the data by itself. The edge comes from how quickly each team can act on it and how much manual work gets removed from the front end of that process.

Sales Teams: Build Sharper Outbound Lists in Less Time

🎯Best for: B2B Sales & Lead Generation Teams

Sales teams lose time before outreach even starts. One rep opens the YC page, another checks LinkedIn, someone else copies notes into a sheet. That handoff is slow, inconsistent, and breaks focus before a single message gets written.

With structured founder data already enriched with LinkedIn URLs, reps can move directly to filtering and personalization. A founder with a strong engineering background in fintech may need a completely different opening than a repeat operator in consumer SaaS. Knowing that before you write saves time and improves reply rates.

The scraper also supports API access so teams that want to skip the manual CSV download entirely can pipe results directly into HubSpot, Salesforce, or any CRM with a simple integration.

💡  Practical use case

Filter exported founders by location and prior employment. Build a short
list of YC founders who previously worked at enterprise software
companies they're more likely to understand and respond to a B2B pitch.
This kind of segmentation is impossible without structured employment
data.

Recruiting Teams: Map Founder Networks and Find Talent Faster

🎯 Best for: Recruiters & Talent Acquisition Teams

Recruiters don't only hire founders they follow founder networks, alumni circles, and early startup clusters. Founder education and employment history often point directly to where strong candidates are concentrated.

If several founders building in one space came from the same companies or universities, that pattern reveals a talent hub worth targeting. Instead of cold outreach across a wide field, a recruiter can focus on the specific clusters where relevant experience is most likely to be found.

Founder data also helps priorities which startups are worth watching for early hiring. A founding team with deep operating experience at strong companies tends to attract strong early hires. That signal is in the employment history and it's now structured and searchable.

"Founder data gives recruiting teams a map, not just a list. The difference between the two is where you spend your time next week."

VC Teams: Screen Markets and Founders With Better Context

🎯 Best for: Venture Capital & Investment Teams

VC firms care about speed, but not blind speed. Fast screening without context wastes partner time. Founder education, prior roles, location, and company theme together give analyst teams enough signal to priorities before the first meeting note is written.

As a concrete example: an analyst team covering climate tech can pull all YC Startup School founders in that vertical, review their employment and education patterns, and identify the strongest signals in under an hour. Without structured data, building that same picture manually takes days.

The scraper also supports ongoing batch tracking. Run it regularly across a sector and you'll start to see patterns founder backgrounds converging around certain schools or companies, geographic clusters emerging, repeat operators returning to a space. These signals are easier to spot when the data is consistent and structured across every run.

💡  Practical use case

Pull all YC Startup School founders in a specific vertical. Cross
reference employment history with your existing portfolio founder
backgrounds. Founders who share career DNA with your best-performing
portfolio companies are a stronger starting point for first
conversations than a cold batch list.

What Automation Does Better Than Manual Research

Manual YC founder research still works at small scale. One analyst can spend an afternoon pulling profiles, matching LinkedIn, and cleaning a spreadsheet. The process is clear. The output is usable.

But it doesn't hold up when volume increases or the research needs to repeat regularly. Here's what breaks down first:

Missed profiles manual processes have natural gaps. Attention fades, tabs get lost, rows get skipped.
Stale data a spreadsheet built on Monday is already incomplete by Thursday. Founders update bios, change roles, add new co-founders.
Inconsistent matching different team members find different LinkedIn profiles for the same person. The data is never quite the same source.
Time cost the hours spent on collection aren't spent on the decisions that actually matter.

Automation improves all four. The same process runs every time, on schedule, with the same matching logic applied to every founder. Output is clean, consistent, and ready to export in JSON, CSV, or Excel directly from Apify.

Where Automation Stops and Human Judgment Begins

Automation is best at collecting facts. People are still better at deciding what those facts mean.

That balance matters more here than in most data tools. The YC Founder Scraper collects and enriches. It can't decide the best opening message for a given founder, make a final hiring call, or replace the investment conviction that comes from a real conversation.

The best setup is simple: let automation handle collection, matching, and export. Let people handle decisions. The data gets you to the starting line faster. What you do from there is still yours to own.

"The point isn't to remove human judgment from founder research. It's to make sure that judgment is applied to the right question — not spent on copy-pasting LinkedIn URLs."

The Bottom Line

Automated YC founder data collection gives sales, recruiting, and VC teams the same core advantage: speed with structure. It shortens the research cycle, improves the quality of targeting, and gives teams time back to focus on the decisions that actually move things forward.

The Y Combinator Founder Scraper by Techforce Global handles the part of founder research that shouldn't require human attention collection, matching, and export. The part that does require human attention strategy, outreach quality, judgment is exactly where your team's time is better spent.

Try the Y Combinator Founder Scraper — Free on Apify
https://apify.com/techforce.global/y-combinator-founder-scraper

Stop Manual searching for trade shows, Here's how we automated it with Apify

Techforce Global — Wed, 25 Mar 2026 11:47:18 +0000

If your team does B2B outreach, partner scouting, or market research, you've probably spent hours manually hunting for trade shows and exhibitions on EventsEye.com filtering by industry, scrolling through regions, copying organizer details one by one into a spreadsheet.

We built the EventsEye Scraper to automate exactly that. This article walks through the problem it solves, how it works technically, what data it pulls, and how to plug it into your existing workflows.

Section 1: The Problem with Manual Trade Show Research

Why Real-Time Trade Show Data Matters for B2B Teams

Trade shows and exhibitions are where deals happen. Industry summits surface emerging vendors. Regional expos reveal market shifts before they hit the news. For B2B teams, being in the right room or knowing who was is a competitive advantage.

Real-time event data helps marketing teams build targeted outreach lists, sales teams connect with decision-makers right after a conference wraps, and strategy teams track where competitors are showing up. The challenge isn't understanding the value of this data. It's collecting it without burning hours doing it manually.

Common Hurdles in Event Monitoring

Manual trade show research is time-consuming in a way that's easy to underestimate until volume picks up. Here's what teams typically run into:

Searching platform by platform EventsEye.com organizes data by industry, region, country, and city, but navigating that hierarchy manually for multiple verticals is slow.
Inconsistent data formats dates, venue names, and organizer contacts look different on every page.
Information that changes overnight a venue update or a rescheduled date can make yesterday's spreadsheet unreliable.
No structured export copying rows into Excel by hand doesn't scale when you need data across 10 industries and 20 countries.

Introducing the EventsEye Scraper by Techforce Global

The EventsEye Scraper is an Apify Actor built by Techforce Global that automates data extraction from EventsEye.com. It supports hierarchical filtering by Industry → Region → Country → City, and returns clean, structured data on exhibitions names, dates, venues, organizer contacts, event URLs, and more.

Unlike generic scrapers or custom scripts that break when a page layout changes, this Actor is maintained and runs reliably on Apify Cloud. You can export results as JSON, CSV, or Excel, or connect directly via API.

Try it here: https://apify.com/techforce.global/events-eye-scraper

Section 2: Technical Setup & Configuration

Setting Up Your Filters for Maximum Relevance

The scraper's core strength is its hierarchical filtering system. Instead of pulling everything and cleaning up later, you define exactly what you need upfront:

Industry — e.g., Automobile, Pharma, Technology, Food & Beverage
Region — e.g., Asia-Pacific, Europe, North America
Country — e.g., India, Germany, USA
City — e.g., Ahmedabad, Munich, Chicago

You can also enable Direct City Mode to fetch all exhibitions in a city across all industries in one run useful for geographic prospecting or event-density analysis.

Detailed Scraping Mode

By default the scraper runs in standard mode pulling exhibition names, dates, locations, and URLs quickly. Enable detailed scraping to also capture:

Organizer name, address, phone, and email
Venue full address and contact details
Event website and registration link

Detailed mode runs slightly slower but is worth enabling when you're building outreach lists or feeding data into a CRM.

Reliability & Anti-Blocking

Apify handles proxy rotation and request scheduling automatically. This means the scraper avoids IP bans and rate limits without any configuration on your end. You can schedule runs on a daily or weekly cadence and trust the output to be consistent.

If a run fails, Apify's dashboard surfaces the error with logs so you can diagnose and re-run without digging through code.

Section 3: What Data You Get

Core Output Fields

Every run returns a structured dataset with the following fields:

Exhibition Name — the full official name of the trade show
Description — brief overview of the event focus
Start & End Dates — precise date range
City, Country, Region — full geographic context
Industry & Category — the vertical it belongs to
Venue Name & Address — location details
Organizer Name & Contact — who runs the show
Event Website & Email — direct links for outreach
Source URL — the EventsEye.com listing for verification

Export Formats

Results are available in JSON, CSV, and Excel directly downloadable from the Apify dataset view. You can also access data via the Apify API for programmatic integration into dashboards, CRMs, or internal tools.

Time zones are standardized in output. Addresses are cleaned for direct use in mapping tools or mail merge systems.

Section 4: Plugging Event Data into Your Workflow

Export Options: From Scraper to Your Stack

The simplest integration is a direct CSV export into Google Sheets shareable across teams in minutes. For ongoing workflows:

Zapier — trigger actions in HubSpot, Notion, or Slack when new exhibitions are scraped
API — pull data directly into a custom app or internal tool
Salesforce — map fields from the CSV export to account or lead records

Event-Driven Marketing Campaigns
Once you have a feed of upcoming exhibitions in your target industries, you can build proactive campaign sequences around them:

Pre-event: Identify organizers and exhibitors 4–6 weeks out. Start outreach referencing the specific show.
During the event: Trigger LinkedIn connection requests or emails to attendees.
Post-event: Follow up with leads who attended with context from the event topic.

Geo-filters make this especially effective for regional teams. Filter exhibitions within your target city or country and send hyper-localized invites or partnership proposals.

Competitive Intelligence Through Event Tracking

Run the scraper monthly across your key industries. Track which exhibitions your competitors are likely to attend or sponsor. Identify gaps events they're missing that represent untapped exposure to your brand.

Cross-reference exhibition organizer contacts your CRM to find warm introductions. A shared industry event is a natural conversation starter.

Conclusion: From Manual Searching to Structured Intelligence

The EventsEye Scraper doesn't just save time it gives your team a repeatable, scalable system for trade show intelligence. The data that used to take hours to compile now takes minutes to generate, and it arrives clean and ready to use.

Key takeaways:

Boost lead quality by targeting decision-makers at relevant exhibitions
Cut hours of manual searching with scheduled automated runs
Filter precisely by industry, region, country, and city no noise
Feed structured data directly into your CRM, dashboard, or outreach tool
Gain competitive intelligence by tracking exhibition patterns over time

Try it free on Apify setup takes under 2 minutes:

https://apify.com/techforce.global/events-eye-scraper

Built with ❤️ for the trade show & event data community by Techforce Global

Questions or feature requests? Reach us at: bhavin.shah@techforceglobal.com

How We Built a Cookie-Free LinkedIn Candidate Search Tool That Saves Recruiters Hours Every Week

Techforce Global — Wed, 11 Mar 2026 12:57:39 +0000

As a developer at @techforce_global , We have spent countless hours helping recruitment teams overcome one of their biggest challenges: finding qualified candidates quickly and efficiently. Today, We are excited to share how we built the LinkedIn Candidate Search (No Cookies) actor on Apify a tool that's transforming how recruiters source talent.

The Problem: Manual LinkedIn Searches Are Killing Productivity

If you're in recruitment or talent acquisition, you know the drill. You need to find 20 Python developers in Ahmedabad, or senior DevOps engineers in London. So you head to LinkedIn, type in your search terms, scroll through pages of results, manually copy names, job titles, and profile URLs into a spreadsheet, and hope you haven't missed anyone.

This manual process isn't just tedious, it's incredibly time-consuming. What should take minutes ends up taking hours, and by the time you're done, you've lost precious time you could have spent engaging with candidates

The Solution: Automated LinkedIn Candidate Search Without the Cookie Headache

We built our LinkedIn Candidate Search actor to solve this exact problem. Here's what makes it different:

No LinkedIn Login Required

Unlike traditional LinkedIn scrapers that require you to provide cookies or log in with your account (and risk getting flagged), our actor operates through external search simulation.
This means:
• Zero risk to your LinkedIn account
• No cookie management hassle
• No authentication headaches
• Completely ethical and compliant

Lightning-Fast Results

Input your parameters, hit run, and within minutes you'll have a clean, structured dataset of candidates. For example:

Input:

{
  "job_role": "Python Developer",
  "location": "Ahmedabad",
  "max_profiles": 20
}

Output:

You get a beautifully formatted dataset with:

• Full candidate names
• Current job titles
• LinkedIn profile URLs
• Profile snippets/bios
All ready to export as JSON, CSV, or Excel, whatever works best for your workflow.

Built for Recruiters, By Developers Who Understand Recruitment

We didn't just build a scraper we built a recruitment tool.

That's why we include:

Curated Job Role Dropdowns: We've pre-populated the most common IT roles so you don't have to worry about search query syntax
Smart Pagination: Automatically handles multiple pages to reach your target profile count
Deduplication: No duplicate profiles cluttering your results
Anti-Detection: Built-in stealth mode ensures consistent, reliable results

Real-World Use Cases

Since launching, we've seen our actor used in fascinating ways:

1. Recruitment Agencies: Building Talent Pools

A recruitment agency specializing in tech placements uses our actor to build talent pools for their clients. They run searches for multiple roles across different cities, creating comprehensive candidate databases they can tap into for future positions.
Result: They've cut their candidate sourcing time by 70% and can respond to client requests within hours instead of days.

2. Startups: Competitive Intelligence

A fast-growing startup uses our tool to identify professionals working at competitor companies. This helps them understand market trends and identify potential hires who already understand their industry.

3. Sales Teams: Lead Generation

B2B sales teams use our actor to identify decision-makers at target companies. By searching for specific job titles in particular locations, they build targeted prospect lists for outreach campaigns.

4. Market Researchers: Industry Analysis

Analysts use our tool to study professional presence and job title distribution across different regions, helping clients understand talent concentration and market dynamics.

How to Get Started in 3 Simple Steps
Getting started with our LinkedIn Candidate Search actor is incredibly straightforward:

Step 1: Access the Actor Head to apify.com/techforce.global/linkedin-candidate-search and click "Try for free"

Step 2: Configure Your Search
• Select your desired Job Role from our curated IT sector dropdown
• Enter the Location you want to search (city or region)
• Set your Max Profiles (up to 50 per run)

Step 3: Run and Export Click "Run" and watch the magic happen. Within minutes, download your results in your preferred format—JSON for developers, CSV/Excel for most recruitment teams.

Pricing That Makes Sense

At $15 per 1,000 results, our actor delivers incredible value. Compare that to:
• Hours of manual searching at your hourly rate
• LinkedIn Recruiter subscriptions (which start at $170/month for basic features)
• Virtual assistant costs for manual data entry
For most recruitment teams, the ROI is clear after the first use.

Technical Excellence Under the Hood

For the developers and tech-curious readers, here's what makes our actor reliable:
• Advanced Search Simulation: We've reverse-engineered LinkedIn's public search mechanisms to provide accurate results without requiring authentication
• Rate Limiting & Throttling: Smart delays and request pacing to ensure long-term reliability
• Human-Like Behavior Patterns: Randomized delays and realistic browsing patterns
• Robust Error Handling: Graceful fallbacks and retry logic for maximum uptime
• Clean Data Structures: Properly formatted, ready-to-use output with consistent schemas

The Future of Recruitment Is Automated

The recruitment landscape is changing. Manual data entry and tedious research are becoming relics of the past. Tools like our LinkedIn Candidate Search actor represent the future: fast, automated, and accessible to teams of any size.
We're constantly improving based on user feedback. Recently added features include:
• Expanded job role options
• Improved snippet extraction
• Faster processing times
• Better deduplication logic
And we have exciting updates planned for the coming months, including support for additional filters and enhanced data enrichment.

Try It Risk-Free Today

Whether you're a solo recruiter, part of a growing startup, or managing talent acquisition for an enterprise, our LinkedIn Candidate Search actor can transform your sourcing workflow.

Ready to see it in action? Visit apify.com/techforce.global/linkedin-candidate-search and try it for free. No credit card is required to test it out.

Got questions or need a specific job role added to our dropdown? Reach out to me directly at bhavin.shah@techforceglobal.com I personally respond to every message.

About the Author

I'm part of the development team at @techforce_global ,where we build automation tools that help businesses work smarter, not harder. Our actor portfolio on Apify includes solutions for event scraping, web crawling, and professional data extraction. Connect with me on LinkedIn to stay updated on our latest releases and recruitment tech insights.

Disclaimer:

This tool extracts publicly available information from LinkedIn search results. Always ensure your use complies with LinkedIn's terms of service and applicable data protection regulations in your jurisdiction.

Mastering Event Scraper: The Definitive Guide to Scraped Tech Event Data

Techforce Global — Wed, 25 Feb 2026 12:56:39 +0000

Finding the right tech events shouldn’t take hours. Scrolling through endless websites and juggling emails just to stay on top of conferences, webinars, and meetups wastes valuable time. Businesses need a smarter way event scraping pulls everything into one place, powering lead generation, competitive intelligence, and market research without the manual effort.

Section 1: Why Automated Tech Event Scraping is Non-Negotiable

The Cost of Manual Event Tracking

Manual tracking means hours lost to spreadsheets and browser tabs, with missed details and wrong dates leading to costly mistakes. Automation fixes this by collecting data quickly and accurately. Multiply your hours spent searching by your hourly rate that’s the ROI from switching to an automated event scraper. It pays off fast.

Competitive Advantage Through Real-Time Intelligence

Real-time event data lets you act before competitors. Spot a rival’s booth at a conference early, and you can plan your response immediately. One sales team secured spots at a sold-out AI summit days ahead of the crowd, connecting with key clients and boosting deals by 20%. Fast event scraping turns raw data into decisive action.

Finding Underserved Market Gaps

Scraped data reveals where events are thin. Few meetups in a city could signal low competition for your services. A niche with no conferences means a fresh opportunity. If scrapers surface sparse cybersecurity events in a region, you could host your own workshop and own that space.

Section 2: Deep Dive into Apify’s Techforce Global All Events Scraper

Architecture and Capabilities

The Techforce Global All Events Scraper on Apify pulls tech events from hundreds of online sources simultaneously. Set inputs like date ranges, keywords such as “AI conference,” or locations like “San Francisco” to filter results. It runs on Apify’s cloud no setup required on your end. Visit the
Apify scraper page for full setup details.

Data Structure: What You Extract

Each run returns event name, date and time, location, speaker info, registration link, and event type delivered in clean JSON or CSV format. Standardized output means no mix-ups: a venue listed as “SF Convention Center” stays consistent across all sources. A single run can surface 500+ events, with speaker bios ready for influencer outreach.

Customization and Scalability

Run the scraper on a daily or weekly schedule in Apify to keep your data fresh automatically. Scale to multiple instances for large jobs one enterprise team used it to track 10,000 global sources monthly. For larger teams, API integration enables fully automated pipeline updates with zero daily effort.

Section 3: Putting Scraped Event Data to Work

Lead Generation and Sales

Connect scraped attendee lists to your CRM for targeted outreach. Speaker profiles become warm leads for demos. One B2B SaaS company reached 200 attendees per day from five conferences follow-ups converted 15% into customers. High registration numbers on a topic signal the exact pitches your prospects want to hear.

Product Roadmapping

Track keywords in event descriptions to catch trends before they peak. Rising mentions of “quantum computing” or “blockchain” signal where the market is heading. One software firm spotted the blockchain wave early, shipped relevant features ahead of rivals, and gained measurable market share as a result.

Marketing Spend and Content Strategy

Gauge event popularity by how often it appears across scraped sources then sponsor the high-traffic ones for maximum exposure. Pick speaking slots at niche events where your audience is concentrated. One marketer cut ad waste by 30% by focusing budgets on events the scraper identified as high-engagement.

Section 4: Ethical and Compliant Web Scraping

Legal and Ethical Boundaries

Always check a site’s robots.txt before scraping and respect its terms of service. Scrape only publicly available data, add delays between requests (around 5 seconds), and avoid overloading servers. Apify builds these safeguards in by default — follow them and you stay compliant.

Data Quality Assurance

After each run, clean and validate your data: standardize dates to MM/DD/YYYY, fill missing venues with “TBD,” remove duplicates, and purge expired events. Spot-check samples against original sources and target 95%+ accuracy. Consistent quality checks keep your scraped event data reliable and actionable.

Conclusion: The Future is Scraped and Automated
Manual tracking can’t keep pace with the tech world. The @techforce_global All Events Scraper makes it easy to gather, clean, and act on event data at scale. The key advantages are clear:
● Automation saves hours previously lost to manual event discovery.
● Deeper data surfaces trends and opportunities others miss.
● Actionable intelligence drives leads, sales, and smarter strategy.
Start scraping today. Your next big opportunity might already be in the data.

Watch the full All Event Scraper Demo on YouTube

All Event Scraper | Techforce Global | @apifyblog