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Spendesk Company Profile

The company surpassed €1 billion in spend under management by 2021 ... Mary Barthe - Communications Manager - mary.barthe@spendesk.com

Industry: FinServ

Segment: FinTech

Team size: 501-1000

Funding: Series B

Competitors: Payhawk, Airwallex

Generated by Kustiq, an AI company intelligence platform for B2B teams. Multi-stage pipeline: AI classification + live web scraping + SMTP-verified contacts + 12-factor rule-based churn engine.

Company profile

Spendesk

The company surpassed €1 billion in spend under management by 2021 ... Mary Barthe - Communications Manager - mary.barthe@spendesk.com
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AI-verified profile

The company surpassed €1 billion in spend under management by 2021 ... Mary Barthe - Communications Manager - mary.barthe@spendesk.com

Employees

501-1000

Funding

Series B

AI Classification

FinServFinTech

Spendesk is a B2B spend management SaaS platform serving 5,000+ finance teams across Europe, offering company cards, expense management, accounts payable automation, and spend controls. It targets CFOs, financial controllers, treasurers, and accountants primarily at SMB and mid-market companies.

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Social Links

Linkedin

Competitors

PayhawkAirwallex

Spendesk is profiled by Kustiq, an AI company intelligence platform for B2B teams. Spendesk is one of 79 fintech companies in our Financial Services directory. Similar profiles include Requested URL cannot be found, Gwi, Dykema, and Appcues.

How Kustiq built this profile

Hybrid pipeline: deterministic verification + Claude reasoning

  • Live web rendering via Browserless headless Chrome, not a stale cached scrape.
  • Search intelligence via Serper API across {company site, LinkedIn, press, GitHub}.
  • Claude Sonnet 4.6 classifies vertical, segment, and growth signals; never used for facts.
  • 12-factor churn engine produces a deterministic risk score with zero LLM calls at scoring time.
  • SMTP RCPT TO handshake verifies every contact at delivery time on full profiles (paid plan).

Read the full methodology in our LLM-readable reference or compare against the deterministic-vs-AI framework.

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