We extract startup profiles, funding rounds, investor portfolios, cap tables, and sector reports from Tracxn. 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 tracxn.com. All fields typed and schema-versioned.
"company_id": "TXN-847291", "name": "FinEdge Tech", "website": "finedgetech.io", "founded_year": 2021, "location": "Bengaluru, India", "stage": "Series A", "employee_count": 145, "sector": "FinTech > Lending"
| # | company_id | name | website | founded_year | location | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Funding & Valuation objects from tracxn.com. All fields typed and schema-versioned.
"round_name": "Series A", "date": "2023-08-14", "amount": 12500000.0, "currency": "USD", "valuation": 65000000.0, "lead_investor": "Sequoia Capital India", "post_money_valuation": 77500000.0
| # | round_name | date | amount | currency | valuation | investors |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Founders & Team objects from tracxn.com. All fields typed and schema-versioned.
"founder_name": "Rahul Sharma", "role": "CEO & Co-founder", "education": "IIT Delhi", "past_companies": "['Flipkart', 'Paytm']", "total_founders": 2, "linkedin_url": "linkedin.com/in/rahulsharma-finedge"
| # | founder_name | linkedin_url | role | education | past_companies | board_members |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Investors objects from tracxn.com. All fields typed and schema-versioned.
"investor_name": "Accel Partners", "type": "Venture Capital", "location": "Palo Alto, CA", "total_investments": 1432, "active_portfolio": 894, "preferred_stages": "['Seed', 'Series A', 'Series B']"
| # | investor_name | type | location | aum | total_investments | notable_exits |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Competitors & Sector objects from tracxn.com. All fields typed and schema-versioned.
"primary_sector": "Financial Technology", "sub_sector": "Alternative Lending", "global_rank": 412, "regional_rank": 14, "competitor_list": "['LendingKart', 'CapitalFloat']", "taxonomy_path": "FinTech > Alternative Lending > SME Loans"
| # | primary_sector | sub_sector | taxonomy_path | competitor_list | market_share_estimate | global_rank |
|---|---|---|---|---|---|---|
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Our Tracxn scraper handles every layer of the platform: company profiles, funding rounds, cap tables, and investor portfolios - with JavaScript rendering, session management, and anti-bot circumvention built in.
Name, description, location, founding year, employee counts, and every metadata field Tracxn surfaces - scraped at the company level.
Capture round names, dates, amounts, valuations, and participating investors - normalised across currencies.
Extract shareholder breakdowns, equity percentages, and dilution metrics where available in the platform.
Full founder profiles, past experience, education history, and board member details - mapped to LinkedIn URLs.
Investor names, fund sizes, preferred stages, and historical investment graphs - for every fund on the platform.
Track primary sectors, sub-sectors, and custom Tracxn taxonomy tags to accurately categorise millions of startups.
Extract direct competitors, alternative solutions, and market landscape positioning for any given company.
Monitor revenue run rates, burn rates, and profitability indicators based on public filings aggregated by Tracxn.
Run one-off bulk exports or configure continuous pipelines at weekly or daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide sector tags, investor names, or company URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for tracxn.com.
Schema validation, null-rate checks, and sample profiles before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Business directories invest heavily in scraping detection. Here is how we stay resilient - and why teams choose managed infrastructure over DIY.
Directory sites operate strict bot detection on TLS fingerprints, browser headers, and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management.
Tracxn company profiles and funding graphs are heavily JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution and lazy-load triggering - capturing data that headless HTTP clients miss.
Platform layouts change frequently. Our selector strategy uses multiple fallback chains per field - CSS selectors, XPath, and text-pattern matching - so a layout change does not break your data pipeline.
For large startup catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs - reducing compute cost, storage bloat, 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.
VC firms monitor new funding rounds, founder movements, and sector trends to identify early-stage investment targets.
PE analysts track competitor landscapes, cap table structures, and historical valuations to evaluate mature startup acquisitions.
Corporate development teams map entire industry taxonomies to spot emerging threats and strategic buyout opportunities.
B2B sales teams use funding events and employee growth metrics as trigger signals to pitch enterprise software.
Consulting firms aggregate funding volumes by sector to publish macroeconomic reports on startup ecosystems.
Founders and strategy leads track rival funding rounds, investor overlap, and executive hires to benchmark growth.
"Tracxn holds the most structured taxonomy of the global startup ecosystem - but none of it integrates with your CRM or data lake unless you build the extraction pipeline."
Most teams underestimate the investment required: reliable directory scraping requires residential proxies, full JavaScript rendering, CAPTCHA handling, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on deal analysis - not the infrastructure.
Everything supported by our tracxn.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.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request with sticky sessions where required.
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 tracxn.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law in India, the US, and the UK. DataFlirt targets only public, non-authenticated company, funding, and investor data. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should review Tracxn Terms of Service 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. Our selectors have multi-layer fallback chains so DOM changes do not break the pipeline.
Yes. We capture every historical funding round listed on a company profile, including round names, dates, amounts, valuations, and participating investors.
Pipelines can be configured to run daily or weekly to capture new funding announcements and profile updates. Change detection ensures you only process new information.
Yes. We extract the complete sector, sub-sector, and tag breadcrumbs for every company, allowing you to recreate their classification system in your own database.
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, field completeness, 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 sector dump or a continuous funding-monitoring feed across 1M startups - we scope, build, and operate the pipeline. Tell us what you need.
