What Pave changed

• Removed a detailed list of data statistics, including counts for base pay benchmarks, equity benchmarks, and countries mapped.
• Replaced a testimonial from the Global Head of Compensation at McDonald’s with a quote from the Compensation Director at LaunchDarkly.
• Swapped a general "thought partner" quote for a specific use-case testimonial about using Pave Agent for promotion scenarios.
• Added a new featured quote from Varun Anand, Co-Founder & Head of Operations.

Pave is shifting its social proof from large enterprise names like McDonald’s to high-growth tech companies like LaunchDarkly. They also removed specific, granular data counts (like the number of job families and countries), which might suggest a move away from competing on raw volume toward competing on AI capabilities and ease of use.

• Global pay differentials decreased from 3,261 to 3,189.
• Org structure benchmarks decreased from 3,140 to 3,066.
• Job posting data decreased from 4,361 to 4,292.
• Countries mapped decreased from 141 to 140.
• Metro areas covered decreased from 566 to 562.
• Job families tracked decreased from 242 to 239.
• Industries covered decreased from 36 to 35.
• Removed the text fragment "Pave is thinking" from the AI agent section.

Pave updated their live dataset numbers, showing a slight decrease in the number of global pay differentials, metro areas, and job families they track. This suggests a cleanup or re-categorization of their real-time market data.

• Added specific data points for their market intelligence, including 812,000 base pay benchmarks and 468,000 equity benchmarks.
• Listed coverage details: 141 countries, 566 metro areas, and 242 job families.
• Added a new interactive prompt example: "What should I pay a P4 Software Engineer?" to demonstrate the Pave Agent.
• Included new categories for tracking, such as global pay differentials and org structure benchmarks.

Pave is moving from general claims about their dataset to showing exact numbers. This makes their market data look more credible and harder to ignore for companies hiring globally.