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Pave

Fintech · New York City, United States · Founded 2012

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Pave is a cutting-edge startup that specializes in compensation management solutions. It operates in the human resources (HR) technology market, providing tools that help companies plan, communicate, and benchmark their employee compensation in real time. Pave's primary clients are businesses of various sizes, ranging from small startups to large enterprises, particularly those that are venture capital (VC) backed.

The core of Pave's offering is its suite of compensation tools that integrate seamlessly with existing HR Information Systems (HRIS), Applicant Tracking Systems (ATS), and Cap Table software. This integration allows for real-time data access, eliminating the need for cumbersome spreadsheets and manual data entry. By leveraging these integrations, Pave enables HR leaders and compensation administrators to make informed decisions based on up-to-date and accurate data.

Pave's business model is likely subscription-based, where companies pay for access to its software platform. This model ensures a steady revenue stream as clients subscribe to use the tools on an ongoing basis. Additionally, Pave may offer premium features or services, such as advanced analytics or personalized support, which could be available at higher subscription tiers.

The company makes money by providing value through its software, which simplifies complex compensation analysis, planning, and communication. This value proposition is particularly appealing to HR departments that need to ensure fair and competitive compensation practices without the administrative burden of traditional methods.

In summary, Pave is revolutionizing the way companies manage compensation by providing a user-friendly, integrated platform that offers real-time data and insights. This allows businesses to make smarter, data-driven compensation decisions, ultimately leading to better employee satisfaction and retention.

Keywords: compensation management, HR technology, real-time data, integration, HRIS, ATS, Cap Table, subscription model, data-driven decisions, employee retention.

Talent graph

Pave's talent

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Team profiled 29 named & role-tagged
Founders Hidden — book a demo/100 quality score
Key operators 8 quality Hidden — book a demo/100
Potential founders Hidden — book a demo likely future founders
Startup mafia Hidden — book a demo alumni-founded startups
Talent movements Hidden — book a demo in · Hidden — book a demo out past 12 months

Source: Dealroom Talent Intelligence.

Investors

16 investors on Pave's cap table

Sourced from Dealroom's funding history. Investor profile links are shown where available.

Seed 3 investors entered at this stage
Maxfield Capital
C4 Ventures
Y Combinator
Series A 6 investors entered at this stage
Series B+ 7 investors entered at this stage
Seer Capital
Index Ventures
Jeff Weiner
Original Capital
Contrary Capital
Tudor Havriliuc
Craft Ventures

Global footprint

Where Pave has talent and traffic

Workforce by country
7 countries with
team presence
🇺🇸 United States92.9%
🇬🇧 United Kingdom3%
🇨🇦 Canada1.8%
🇻🇳 Vietnam0.6%
🇮🇳 India0.6%
Top 5 of 7 shown
Web traffic by country
170K monthly visits
across markets
🇺🇸 United States59.2%
🇮🇳 India7.8%
🇨🇦 Canada4.4%
🇬🇧 United Kingdom4.1%
🇦🇺 Australia2.6%
Top 5 markets shown

Patent intelligence

$251k patent portfolio · 3 active families

Estimated portfolio value
$251k
0.02% of valuation · 18× smaller than top peer AlphaSense ($5M)
3 active patent families
Where Pave innovates Data miningElectronic communicationFeature (machine learning)Message passingNetwork mapping

Where Pave concentrates vs peers

Each axis is a top-10 topic across Pave and its closest peers. #1 = 100, #10 = 10, absent = 0.

Compare against

Source: Dealroom Patent Intelligence · Patsnap. Request a demo for the full per-sub-category breakdown, individual records, and quality-score history.

Market sentiment

What the market is saying about Pave

An AI-synthesized read of the highest-engagement posts about Pave on X over the past 7 days. We rank by likes & retweets, ignore corporate channels, and surface the themes that broke out from real people.

Reading the room on X — pulling top posts and synthesizing themes…

Source: X recent search ranked by engagement (likes + retweets) · Synthesis by Claude · Cached for 1 hour

See Pave on the full Dealroom platform

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