Pave has announced a significant expansion of the Pave Agent, its AI compensation analyst. The Agent can now reason across a company's full compensation context: every merit cycle in Compensation Planning, the pay ranges and survey methodology built in Market Pricing, a new real-time job posting dataset, and the customer's own documents.
Pave Agent now analyzes merit cycles, pay ranges, real-time job postings, and uploaded documents.
Provides traceable recommendations with cited sources and reasoning.
Analyzes public job posting data refreshed daily for real-time market signals.
Supports file uploads up to 100MB for persistent compensation context.
Data is tenant-secured, isolated, and never used for model training.
Available today to Pave customers.
Most compensation questions cannot be answered from a single source. Adjusting a range means weighing market movement, survey blend, and recent cycle history at once. With this release, that analysis happens in minutes — and the Agent shows its work. Every recommendation cites its sources and explains its reasoning. The Pave Agent can now reference every merit cycle run in Pave Compensation Planning, as well as employee compensation history, allowing compensation teams to complete cycle retrospectives in minutes and bring an employee's full history across cycles into a single pay decision.
The Agent now works from Market Pricing — a company's survey uploads, data rules, blending logic, and every version of its band sets. The Pave Agent can also analyze Pave's public job posting dataset — job titles, descriptions, and posted salary ranges from publicly available listings — enriched with location and job family details and refreshed daily. Compensation teams are using it to price roles in unfamiliar industries, sharpen job descriptions, and track how posted ranges for a role shift over time. The Agent also works with documents that live outside any single platform: pay philosophy, equity plan design, third-party survey files, and offer letters, with files secured to the customer's tenant and separated from other customers.
"Compensation leaders don't need an AI that answers questions in isolation. They need one that understands how their organization actually pays people — their philosophy, their leveling, their history," said Matt Schulman, CEO and founder of Pave. "That's the difference between a generic AI tool and an agent that was purpose-built for compensation. The Pave Agent brings the analysis. Your team brings the judgment."
About Pave
Pave is the AI compensation platform, purpose-built for compensation leaders. Through real-time connections to HRIS, ATS, and equity systems, Pave gives teams a continuously current data foundation for benchmarking, building pay ranges, running merit cycles, and communicating compensation to employees. The Pave Agent, Pave's AI compensation analyst, reasons across that foundation to deliver explainable, advisory recommendations that compensation teams can defend to their CEO, board, and employees. Pave is headquartered in San Francisco.
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