We extract startup profiles, growth metrics, funding histories, and investor portfolios from Mattermark. 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 mattermark.com. All fields typed and schema-versioned.
"company_name": "Stripe", "domain": "stripe.com", "founded_year": 2010, "location": "San Francisco, CA", "employee_count": 7000, "stage": "Late Stage VC", "growth_score": 1452, "mindshare_score": 984
| # | company_id | company_name | domain | description | founded_year | location |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Funding Rounds objects from mattermark.com. All fields typed and schema-versioned.
"round_type": "Series I", "amount": 6500000000.0, "currency": "USD", "date": "2023-03-15", "lead_investor": "Andreessen Horowitz", "post_money_valuation": 50000000000.0, "participating_investors": "['Founders Fund', 'Thrive Capital']"
| # | company_id | round_type | amount | currency | date | lead_investor |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Investor Profiles objects from mattermark.com. All fields typed and schema-versioned.
"investor_name": "Sequoia Capital", "investor_type": "Venture Capital", "location": "Menlo Park, CA", "active_portfolio_size": 452, "total_investments": 1284, "exits": 312, "notable_investments": "['Apple', 'Google', 'Airbnb']"
| # | investor_id | investor_name | investor_type | location | active_portfolio_size | total_investments |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Growth Metrics objects from mattermark.com. All fields typed and schema-versioned.
"timestamp": "2026-05-12T00:00:00Z", "growth_score": 1452, "mindshare_score": 984, "employee_count": 7000, "web_traffic_rank": 1245, "social_followers": 452100
| # | company_id | timestamp | growth_score | mindshare_score | employee_count | web_traffic_rank |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Executive Team objects from mattermark.com. All fields typed and schema-versioned.
"person_name": "Patrick Collison", "title": "CEO & Co-Founder", "linkedin_url": "https://linkedin.com/in/patrickcollison", "start_date": "2010-01-01", "previous_company": "Auctomatic", "previous_title": "Co-Founder"
| # | company_id | person_name | title | linkedin_url | start_date | previous_company |
|---|---|---|---|---|---|---|
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Our Mattermark scraper handles every layer of the platform: company profiles, proprietary growth scores, funding histories, and investor portfolios — with pagination management and session handling built in.
Extract proprietary Mattermark Growth Score and Mindshare Score over time to track startup momentum and identify breakout companies early.
Capture every funding event, including round type, amount raised, date, lead investors, and participating syndicates.
Map VC and PE firms to their portfolio companies, tracking active investments, total exits, and co-investment networks.
Extract foundational company data: domain, founding year, location, stage, and detailed business descriptions.
Track hiring velocity by capturing employee count changes over time, a leading indicator for company growth.
Scrape similar companies and competitor lists to map out market landscapes and industry clusters.
Extract leadership team details, including names, titles, and professional profiles for targeted outreach.
Capture detailed category tags and industry classifications to filter and segment target markets accurately.
Run one-off bulk exports or configure continuous pipelines at weekly or monthly cadences with change-detection diffing.
Brief in. Clean data out.
Provide target industries, investor names, or specific company domains. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and pagination logic for mattermark.com.
Schema validation, null-rate checks, and sample profile reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Mattermark protects its proprietary data with strict rate limits and pagination walls. Here's how we stay resilient.
Mattermark monitors request velocity and IP reputation. Our crawlers use residential proxies with realistic browser fingerprints and randomised request timing to avoid IP bans and rate limiting.
Mattermark relies heavily on JavaScript for rendering charts, growth scores, and paginated lists. We run full Playwright browser sessions to ensure complete data extraction.
Our selector strategy uses multiple fallback chains per field — CSS selectors, XPath, and text-pattern matching — so minor layout changes do not interrupt your data feed.
For large company catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, schema drift, and coverage drops — and respond before you notice.
Venture capital firms track Mattermark Growth Scores and employee velocity to identify breakout startups before they raise their next round.
Sales teams filter companies by funding stage, employee count, and industry to build highly targeted account-based marketing lists.
Analysts map out industry landscapes, tracking funding trends and category leaders to understand market dynamics.
Companies track their competitors' funding events, hiring velocity, and Mindshare scores to benchmark their own performance.
ML teams use startup firmographics and funding histories to train predictive models for startup success and valuation.
PE firms evaluate target companies by analysing historical growth metrics, investor syndicates, and market positioning.
"Mattermark aggregates critical startup growth signals, but integrating their proprietary scores into your CRM requires dedicated extraction infrastructure."
Most teams underestimate the investment required: reliable Mattermark scraping requires handling complex pagination, JavaScript rendering, session management, and strict rate limits. DataFlirt absorbs that complexity so your analysts can focus on sourcing deals — not building infrastructure.
Everything supported by our mattermark.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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). 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 mattermark.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law, reinforced by the hiQ v. LinkedIn ruling. DataFlirt targets only public, non-authenticated company profiles and funding data. We do not circumvent authentication walls or extract personal data. Clients should review Mattermark's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate limit responses in real time and trigger pool rotation automatically.
Yes, we extract the Growth Score and Mindshare Score as displayed on public company profiles, allowing you to track changes over time.
Pipelines can be configured to run daily, weekly, or monthly depending on your requirements. Change-detection diffs ensure you only process updated records.
Our smallest packages start at a defined list of 5,000 companies or specific investor portfolios with weekly delivery. Contact us with your use case for a scoped quote.
Absolutely. 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 before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of a specific industry or a continuous feed of startup growth metrics — we scope, build, and operate the pipeline. Tell us what you need.
