Procure AI

AI-native procurement automation software
Last updated:
January 23, 2026
Company details
HQ
HEADCOUNT
25-99
ORG TYPE
Startup
SECTOR
Technology & Digital
About the company
Procure AI is a Europe-based software company building an AI-native procurement automation platform for enterprises. Public materials describe agentic AI plus predictive analytics to automate workflows like intake, sourcing, and negotiations. In November 2025, Procure AI announced a $13m seed round to expand engineering and go-to-market, alongside reporting rapid revenue growth over the prior year. The company positions itself as remote-friendly with periodic in-person offsites in Europe.
Locations and presence
Procure AI lists London as headquarters, with additional presence shown in Germany and France. Company communications also reference operations across London, Paris, and Frankfurt.
Palpable Score
56.0
/ 100
Procure AI is one of the clearer “startup-equivalent” cases for grads because at least one current engineering role explicitly targets recent graduates and 0.5–3 years’ experience, with hands-on learning called out. The limiting factor is outcomes evidence: there is little public data on junior progression, retention, or pay ranges, so several pillars cap out.
Pillar 1: Early-career access

Score

13.0
/ 20
  • The company advertises a Software Engineer role “at the start of their career” and “ideally someone who graduated within the past year,” with 0.5–3 years’ experience including internships.
  • Procure AI previously ran a Technical Customer Support Engineer role where prior customer-facing experience was “a plus but not required,” which is another legitimate junior entry point.
  • The company’s wider public role mix still leans senior, so early-career access looks real but not yet frequent or high-volume.
Pillar 2: Hiring fairness and transparency

Score

13.0
/ 20
  • The company’s early-career Software Engineer listing is unusually explicit on scope, including “what you’ll work on” and a clear skills bar for grads.
  • Procure AI lists concrete responsibilities and requirements for the Technical Customer Support Engineer role (bug diagnosis, bug fixing, documentation, collaboration) rather than vague “wear many hats” language.
  • The company does not publish hiring stages, expected timelines, or assessment format in the postings reviewed, and compensation is described as “competitive” without a range.
Pillar 3: Learning and support

Score

12.0
/ 20
  • The company explicitly frames the early-career Software Engineer role as “learn by doing,” including hands-on exposure to DevOps, CI/CD, performance tuning, and enterprise software delivery.
  • Procure AI states the Technical Customer Support Engineer works closely with senior engineers and the product team, plus includes “continuous learning” as a named responsibility.
  • The company references openness to feedback and growth values, but public postings stop short of specifics like onboarding plans, mentorship structure, or review cadence.
Pillar 4: Pay fairness and stability

Score

11.0
/ 20
  • The company advertises full-time roles and lists tangible benefits like 30 days of vacation and regular team offsites, which usually correlate with more stable employment conditions.
  • Procure AI uses “competitive compensation package” wording, but the lack of salary bands makes it hard for early-career candidates to judge fairness without backchannel info.
  • The company mentions flexibility (including limited “work from anywhere” time in at least one posting), but equity terms and junior-level pay positioning are not clearly explained.
Pillar 5: Early-career outcomes

Score

7.0
/ 20
  • The company has repeat hiring signals across time (a support-engineering role appearing in 2024, engineering hiring in 2025, and an explicitly graduate-targeted Software Engineer role in January 2026).
  • Procure AI reports team growth to 40+ across European hubs after the November 2025 seed round, but that does not break out early-career retention or promotions.
  • The company has limited publicly attributable outcome data such as junior promotion stories, calibrated performance paths, or a meaningful volume of early-career reviews, so outcomes scoring stays cautious.
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