Dealroom Scraper: Funding & Investor Data in 2026

The round history is the signal

Dealroom is the database venture funds, corporates and governments use to track startups and investors, comparable to Crunchbase with especially deep coverage of European companies and funds. One profile packs a company’s round-by-round funding history, valuation, revenue KPIs, founders, headcount and web traffic.

The problem is the format: profiles are read one at a time, and a twenty-round funding history does not survive a copy-paste. For deal sourcing or CRM enrichment, you need each profile as a row.

What you can extract

Every profile becomes one row with the same 115 columns, companies and investors alike:

• Identity and status: name, tagline, website, HQ, growth stage, industries, business model
• Funding: every round (type, amount, currency, date, valuation, investors), last round, investor list, ownership mix
• Valuation and financials: latest valuation with range and date, revenue, yearly KPIs (growth, EBITDA, margins, valuation multiples)
• Team: founders and key people with roles, LinkedIn, education and work history; monthly headcount history
• Traffic: monthly web visits, traffic sources, top countries, monthly traffic history
• Investor activity: investments, exits, LP relationships, country, sector and round experience
• Other: legal entities, tech stack, recent news, patents, launch dates

The technical challenge

Parsing is the easy part. Inputs and edge cases are not:

• Input matching: most teams hold domains or names, not Dealroom links. Mapping figma.com to the right profile, and skipping a domain that matches nothing instead of returning another company, is the hard part.
• Nested depth: a founder carries 21 fields, a funding round 8, a KPI year 12. Flatten carelessly and the history disappears.
• Missing versus zero: an unpublished revenue must read as null, not 0, or your averages are wrong.
• Deduplication: the same profile reached through a link and a name must land once.

DIY or a ready-made Actor?

Our Dealroom Scraper on Apify takes Dealroom company or investor URLs, website URLs, bare domains and plain names (name search covers investors and well-known companies; use a URL or domain for smaller ones), with toggles for team, funding rounds and news and a maxItems cap. No Dealroom account, login or API key is needed. Pay-per-result at about $0.004 per record.

A practical workflow

1. Start from what you have: a CRM export of domains, a list of fund names, or Dealroom links.
2. Trim to need: turn off team and news when you only want firmographics; rows come back faster.
3. Track over time: schedule a weekly watch list and compare lastRoundDate, latestValuation and employeesLatest on Dealroom’s stable id; a pipeline that doesn’t break matters more than the first run.
4. Verify the entity: legalEntities gives the registered name and trade register number when Dealroom lists them; check them against Companies House or North Data registry records before a deal.

Use cases that pay off

• Deal sourcing: screen startups by growth stage, last round and headcount growth.
• Investor research: before a raise, see a fund’s notable investments, exits and the sectors and rounds it backs most.
• CRM enrichment: funding, valuation, revenue, headcount and traffic joined back on the domain, feeding automated lead generation.
• Portfolio mapping: feed each notable investment’s path back in as a Dealroom URL to pull a fund’s full portfolio.

The legal boundaries

• Public pages only: the Actor does not bypass any login, paywall or CAPTCHA; Dealroom’s terms of service still govern how you use the data.
• Founders are people: names, roles, education and LinkedIn links are personal data under GDPR; keep only what your use case needs.
• Informational, not audited: valuations and revenue are what Dealroom publishes; verify them before any regulated decision.