We extract company profiles, funding histories, investor portfolios, and growth signals from Dealroom.co. 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 dealroom.co. All fields typed and schema-versioned.
"name": "Revolut", "hq_location": "London, UK", "launch_year": 2015, "operating_status": "Active", "employees_count": 7500, "total_funding_eur": 1500000000
| # | company_id | name | website | hq_location | launch_year | operating_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Funding Rounds objects from dealroom.co. All fields typed and schema-versioned.
"round_type": "Series E", "date": "2021-07-15", "amount_eur": 800000000, "valuation_eur": 33000000000, "investors": "['SoftBank Vision Fund 2', 'Tiger Global Management']", "lead_investors": "['SoftBank Vision Fund 2']"
| # | round_id | company_name | round_type | date | amount_eur | valuation_eur |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Investor Profiles objects from dealroom.co. All fields typed and schema-versioned.
"name": "Sequoia Capital", "type": "Venture Capital", "hq_location": "Menlo Park, USA", "active_portfolio_size": 1245, "exits_count": 312, "unicorns_count": 85
| # | investor_id | name | type | hq_location | funds_raised_eur | active_portfolio_size |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Growth Signals objects from dealroom.co. All fields typed and schema-versioned.
"employee_growth_6m": 12.5, "employee_growth_12m": 28.4, "web_traffic_monthly": 4500000, "job_openings": 342, "github_commits": 1205, "date": "2026-05-12"
| # | company_id | date | employee_growth_6m | employee_growth_12m | web_traffic_monthly | app_downloads_30d |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Founders & Team objects from dealroom.co. All fields typed and schema-versioned.
"name": "Nikolay Storonsky", "role": "Co-Founder & CEO", "company_name": "Revolut", "past_companies": "['Credit Suisse', 'Lehman Brothers']", "board_seats": 1, "education": "['Moscow Institute of Physics and Technology']"
| # | person_id | name | role | company_name | linkedin_url | past_companies |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Dealroom scraper handles complex graph relationships, dynamic charting, and pagination limits — delivering a clean, relational dataset of the global tech ecosystem.
Extract HQ location, founding year, employee counts, operating status, and business models for millions of startups.
Capture round-by-round funding histories, lead investors, round types, and Dealroom's valuation estimates.
Map venture capital and private equity portfolios, tracking active investments, exits, and unicorn counts per fund.
Extract employee growth percentages, web traffic estimates, and hiring velocity metrics to identify breakout companies.
Scrape executive team details, founder backgrounds, and board member networks across the tech ecosystem.
Extract Dealroom's specific industry categorisation, sub-industries, and thematic tags like FinTech, SaaS, or DeepTech.
Capture the similar companies graph to map out competitive landscapes and sector adjacencies automatically.
Extract technological footprint data, patent counts, and infrastructure tools listed on company profiles.
Extract data across EMEA, NAM, APAC, and LATAM ecosystems with normalised currency conversions to EUR/USD.
Run continuous pipelines that only emit records when a company raises new funding or updates its employee count.
Brief in. Clean data out.
Provide target sectors, investor lists, or geography filters. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for dealroom.co.
Schema validation, null-rate checks, and funding-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Dealroom relies heavily on dynamic rendering, complex graph relationships, and strict rate limits. We handle the extraction complexity so you just query the data.
Dealroom is a single-page application heavily reliant on React and GraphQL. We use Playwright to execute JavaScript, hydrate charts, and intercept raw XHR responses for cleaner data extraction.
Startups, investors, and funding rounds are deeply interconnected. Our schema normalises these relationships, mapping company IDs to investor IDs without duplicating nested arrays.
Dealroom employs strict rate limiting and IP reputation checks. We route requests through EU-based residential proxies with realistic browser fingerprints to maintain uninterrupted access.
Funding rounds are reported in local currencies. We extract both the raw reported amount and the Dealroom-converted EUR/USD values to ensure downstream aggregations remain accurate.
For venture capital clients tracking thousands of companies, we maintain state across runs. Our diffing engine only delivers new funding rounds or significant employee growth spikes.
VC firms ingest growth signals and funding histories to identify breakout startups before they begin raising their next round.
Consultancies and strategy teams extract entire sector taxonomies to map market share, funding concentration, and emerging sub-industries.
Corporate development teams monitor competitor funding events, valuation markups, and key executive hires in real time.
B2B SaaS sales teams use employee counts, tech stack data, and funding events as intent signals to prioritise outbound campaigns.
Limited Partners analyse fund performance by scraping investor portfolios, calculating exit velocities, and tracking unicorn creation rates.
Researchers aggregate macro-level funding data across regions to study innovation ecosystems and capital deployment trends.
"Dealroom maps the DNA of the global tech ecosystem, but extracting that graph into a relational warehouse requires specialised infrastructure."
Building a DIY scraper for Dealroom means constantly battling React DOM changes, GraphQL schema updates, and IP bans. DataFlirt manages the entire extraction lifecycle — from residential proxy rotation to schema validation — delivering clean, normalised firmographic data so your analysts can focus on market intelligence rather than pipeline maintenance.
Everything supported by our dealroom.co scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Instead of parsing complex React DOM trees, our Playwright implementation intercepts underlying GraphQL and REST API calls, ensuring higher data fidelity and resilience to UI changes.
We maintain pools of residential ISP proxies across European and North American regions. Rotation happens per-request with sticky sessions where required to prevent rate-limiting.
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 dealroom.co scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available firmographic data is generally permissible under applicable law. DataFlirt targets only public company profiles, funding announcements, and investor data. We do not bypass authentication to extract paywalled proprietary models or personal contact information.
We use Playwright to execute JavaScript and intercept the underlying XHR/GraphQL requests that populate the UI. This allows us to extract the raw, precise data points rather than attempting to parse SVG or canvas elements.
Yes. We can configure the pipeline to target specific Dealroom taxonomies, such as European FinTechs, US-based SaaS companies, or startups tagged with specific deep-tech categories.
We can run pipelines on daily or weekly cadences. Because we use change-detection logic, your warehouse is updated within hours of Dealroom indexing a new funding round or employee growth metric.
Yes. Our extraction schema uses relational IDs. A funding round record will contain the Dealroom company ID and an array of investor IDs, allowing you to easily join tables in your warehouse.
Our engagements typically start with a defined set of target sectors or a list of thousands of company URLs, delivered weekly. We scope the pricing based on the total volume of profiles and the required delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete sector map or continuous funding alerts across the tech ecosystem — we scope, build, and operate the pipeline. Tell us what you need.
