BUSINESS INTELLIGENCE
Wellfound data scraping service.
Structured startup data for VC deal flow, BD prospecting, and market mapping. Funding rounds, team data, and growth signals.
The problem.
WHY THIS IS HARDER THAN IT LOOKS
Seed-stage VCs, accelerators, and BD teams prospecting into the startup ecosystem need structured data from Wellfound (formerly AngelList). The platform has one of the largest startup databases, but its search and export tools are limited for bulk research and systematic deal sourcing.
The core problem with manual VC deal flow is signal-to-noise. Browsing Wellfound manually, an analyst might review 30 to 50 profiles in an hour. At that rate, screening 5,000 startups in a vertical takes weeks. And manual review misses timing signals: you don't see which companies just posted their first engineering hires, which ones raised a round 3 months ago but haven't announced yet, or which founders have quietly added two ex-Stripe employees in the last 30 days. Structured data solves the volume problem and surfaces the timing signals that make the difference between seeing a deal early versus seeing it after the round closes. The VC firm I work with runs a weekly analyst review meeting using the diff report as the agenda, which replaced a manual browsing session that previously took 4 to 5 hours per week.
The data challenge for VC analysts: you need to screen thousands of startups by vertical, funding stage, team size, location, and growth signals. Doing this manually on Wellfound means clicking through profiles one at a time. Exporting is limited. Cross-referencing with Crunchbase or LinkedIn requires manual matching.
For a seed-stage VC firm I work with, the pipeline tracks 30,000 startups across 3 target verticals with weekly diff reports capturing new funding events, team changes, and growth signals. Over the course of the engagement the data directly sourced 12 investments and captured 840 funding events that the firm's analysts used for deal flow prioritization.
This service builds the same sourcing pipeline for your firm.
Is this right for you?
GOOD FIT IF ANY OF THESE SOUND LIKE YOU
You are a seed or early-stage VC firm sourcing deals and need systematic startup screening beyond manual browsing
You are an accelerator evaluating applicants and need structured competitive landscape data
You are a BD team prospecting into the startup ecosystem by vertical, location, or funding stage
You want weekly alerts on new funding rounds, team changes, and emerging companies in your target verticals
What you receive.
EXACT FIELDS, DELIVERED IN YOUR FORMAT
Sample record.
{
"company_name":"Figma",
"website":"https://figma.com",
"description":"Collaborative design tool for building meaningful products",
"vertical":"Design Tools",
"location":"San Francisco, CA",
"team_size":"501-1000",
"funding_stage":"Series E",
"total_raised":"$332.9M",
"latest_round_date":"2024-06-15",
"founders": ["Dylan Field","Evan Wallace"],
"hiring_active": true,
"extracted_at":"2026-04-14T09:00:00Z"
}Straightforward pricing.
SCALE DETERMINES PRICE · NO HIDDEN FEES
Vertical snapshot
One-time extraction of startups in specific verticals or stages. Delivered in 3 to 5 days.
- Up to 5,000 startups
- Full profile data
- Funding history
- CSV, JSON, or Google Sheet
Deal flow monitoring
Weekly tracking with funding event alerts and team change detection.
- Up to 15,000 startups tracked
- Funding round alerts
- New company detection
- PostgreSQL + diff reports
Research partnership
Larger universes, custom classification, and cross-source enrichment.
- Unlimited startups
- Custom vertical taxonomy
- LinkedIn cross-reference
- Scoping call required
Frequently asked questions.
EVERYTHING YOU NEED TO KNOW
For the running VC client the pipeline tracks 30,000 startups across 3 verticals. For larger engagements the full Wellfound database is accessible. The number of trackable startups depends on your target verticals and filtering criteria.
Yes. Weekly diff reports capture new funding events including round type, amount raised (when disclosed), and date. For the running engagement the pipeline has captured 840 funding events over the tracking period. Funding alerts can be delivered via email or Slack the day they appear.
LinkedIn cross-referencing (matching founders and team members to LinkedIn profiles) is available at the Research Partnership tier. Crunchbase data is a separate source but the output schema can be designed to merge cleanly with Crunchbase exports you already have.
Growth signals include: new job postings (the company is hiring), team size increases, new product launches listed on the profile, and funding round announcements. These are the signals VC analysts use to identify companies in an active growth phase.
For deal flow monitoring the pipeline refreshes weekly. New startups, funding events, and profile changes from the past 7 days are captured in each run. The diff report shows exactly what changed since the last delivery.
A vertical-specific snapshot starts at $199 for up to 5,000 startups. Deal flow monitoring starts at $499 per month for up to 15,000 startups with weekly diff reports. Research partnerships scope per engagement.
The VC firm used the weekly funding alerts and growth signal data to identify companies entering fundraising windows. The structured data allowed analysts to filter by vertical, stage, and growth trajectory, surfacing opportunities they would not have found through manual Wellfound browsing. 12 of their investments during the tracking period were first identified through the pipeline data.
Yes. The weekly diff report flags changes in team size, new team members added to the Wellfound profile, and departures. For early-stage startups this is a meaningful signal: a company that adds a CFO and a VP of Sales in the same month is likely preparing for a fundraise or a commercial push. Team composition changes are included in the Deal Flow Monitoring tier as part of the weekly diff.
Yes. Funding stage filtering (pre-seed, seed, Series A, Series B+) and geography filtering (US metro, country, region) are both configured during setup. The VC firm I work with filters by 3 verticals and 2 geographies simultaneously. If your thesis has specific stage or geo constraints, those become the primary filters so you only receive startups that fit your investment mandate.