We extract company profiles, funding rounds, investor portfolios, and M&A activity from PitchBook public directories. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Company Profiles objects from pitchbook.com. All fields typed and schema-versioned.
"name": "Stripe", "website": "stripe.com", "hq_location": "San Francisco, CA", "founding_year": 2010, "primary_industry": "FinTech", "total_raised": 8700000000
| # | company_id | name | website | hq_location | founding_year | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Funding Rounds objects from pitchbook.com. All fields typed and schema-versioned.
"company_name": "Stripe", "round_type": "Series I", "announced_date": "2024-02-15", "deal_size": 6942000000, "lead_investors": "['Sequoia Capital']", "currency": "USD"
| # | round_id | company_name | round_type | announced_date | deal_size | pre_money_valuation |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Investor Profiles objects from pitchbook.com. All fields typed and schema-versioned.
"name": "Andreessen Horowitz", "investor_type": "Venture Capital", "aum": 35000000000, "hq_location": "Menlo Park, CA", "active_portfolio_count": 412, "exits_count": 128
| # | investor_id | name | investor_type | aum | hq_location | website |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for M&A Transactions objects from pitchbook.com. All fields typed and schema-versioned.
"target_company": "Figma", "acquirer_company": "Adobe", "deal_date": "2022-09-15", "deal_size": 20000000000, "deal_type": "Acquisition", "advisors": "['Qatalyst Partners']"
| # | deal_id | target_company | acquirer_company | deal_date | deal_size | deal_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Executive Teams objects from pitchbook.com. All fields typed and schema-versioned.
"full_name": "Patrick Collison", "current_title": "CEO", "company_name": "Stripe", "board_seats": 2, "previous_companies": "['Auctomatic']", "location": "San Francisco, CA"
| # | person_id | full_name | current_title | company_name | board_seats | previous_companies |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our PitchBook scraper navigates strict anti-bot measures to extract accurate company demographics, funding histories, and investor mapping from public directory pages.
Extract founding year, HQ location, employee counts, and primary industry classifications for millions of private entities.
Capture deal sizes, round types, announcement dates, and participating investors for VC and PE transactions.
Map venture capital and private equity firms to their active investments and historical exits.
Track acquisitions, buyouts, and mergers with target details, acquirer data, and deal valuations.
Identify founders, C-suite executives, and board members associated with specific private companies.
Extract PitchBook's suggested competitor arrays to build market landscape models automatically.
Scrape public fund close sizes, vintage years, and LP commitments where disclosed in directories.
Maintain a hash index of profile states. We only deliver records that changed since the last pipeline run.
Bypass Datadome and Cloudflare protections using residential proxies and TLS fingerprint spoofing.
Brief in. Clean data out.
Provide company URLs, investor names, or industry filters. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and CAPTCHA handling for pitchbook.com.
Schema validation, null-rate checks, and sample profile extraction before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
PitchBook deploys aggressive scraping detection and limits public directory access. Here is how we maintain extraction uptime.
PitchBook uses strict Web Application Firewalls. Our crawlers route traffic through ISP-grade residential proxies, matching browser fingerprints to IP locations to avoid automated blocks.
Public directories limit pagination depth. We bypass this by programmatically partitioning the search space using granular alphabetical and industry filters, ensuring total catalogue extraction.
PitchBook frequently updates its HTML structure and obfuscates class names. We use XPath, text-pattern matching, and structural heuristics to maintain schema stability.
For massive company lists, we hash the last-seen profile state. Subsequent runs only push diffs, reducing downstream processing load and storage costs.
Every run emits structured logs to our observability stack. We alert on null-rate spikes and block rates, adjusting proxy pools before data delivery is impacted.
Venture capital and private equity firms monitor funding velocity and executive changes to identify early investment targets.
Corporate development teams track competitor funding rounds and M&A activity to model industry consolidation.
Startups monitor rival fundraising sizes and lead investors to optimise their own pitch strategies.
Executive search firms map leadership teams across high-growth sectors to source candidate pipelines.
Limited Partners track historical fund performance and active portfolio counts to evaluate GP commitments.
B2B sales teams enrich Salesforce records with total funding raised and primary industry classifications.
"PitchBook aggregates the private markets, but mapping that graph into your own systems requires continuous extraction at scale."
Extracting private market data requires navigating strict rate limits, CAPTCHA walls, and obfuscated directory structures. DataFlirt manages the residential proxy rotation and session handling required to pull clean company and investor data without interrupting your engineering workflows.
Everything supported by our pitchbook.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright handles JavaScript rendering and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About pitchbook.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from PitchBook public directories is generally permissible. DataFlirt targets only public, non-authenticated company, funding, and investor data. We do not circumvent authentication walls or violate enterprise subscription terms.
We use residential ISP proxies and full Playwright browser sessions with realistic fingerprints. We monitor for CAPTCHA rate spikes in real time and trigger pool rotation or solver queues automatically.
No. Full cap tables, detailed post-money valuations on unannounced rounds, and exact equity splits are gated behind PitchBook's authenticated enterprise paywall. We only extract what is visible on public profile pages.
Pipelines can be configured to run daily, weekly, or monthly. We track changes via hash indexes and deliver diffs on your specified cadence.
Our smallest packages start at a defined list of 5,000 companies or investors with weekly delivery. For larger directories, we price based on volume and delivery frequency.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series record of acquisitions, buyouts, and mergers as they appear on public profiles.
Yes. We provide a sample run of up to 500 company profiles as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a specific list of 10,000 startups or continuous monitoring of venture capital directories, we build and operate the pipeline. Tell us what you need.
