OVI is the AI-native ATS where you chat to hire. Two AI agents — Sora for sourcing and Milo for screening — find candidates, score every CV, and run AI interviews, all controlled from a single chat.

What does "chat to hire" mean?

Chat to hire is OVI's AI-native hiring workflow: instead of clicking through a traditional ATS, you describe what you need in chat and OVI's AI agents source candidates, screen applicants, run AI interviews, and send outreach for you.

What is an AI-native ATS?

An AI-native ATS is an applicant tracking system built around AI agents rather than forms and pipelines. OVI's AI chat ATS lets recruiters source, screen, interview, and follow up through conversation — the agents do the work and surface only the candidates worth meeting.

Who are Sora and Milo?

Sora is OVI's AI sourcing agent — she scans LinkedIn, the open web, and your ATS history to find best-fit candidates and reach out in your name. Milo is OVI's AI screening agent — he scores every applicant against your rubric and surfaces a ranked shortlist.

How do OVI's AI interviews work?

OVI runs async AI interviews for every shortlisted candidate and returns scores, transcripts, and recordings — so your team only meets the people worth meeting.

Your Salary Benchmarks Are 12 Months Old: How Pave Replaces Compensation Surveys With Real-Time AI

2026-06-19

Picture this: your VP of Engineering needs a market-rate offer for a senior ML engineer by Friday. You pull up your latest Radford data — and realize the survey closed nine months ago, before the latest wave of AI hiring drove base salaries up 15%. You are pricing a 2026 role with 2025 data, and your best candidate just accepted a competing offer.

This is the core problem Pave set out to solve. The New York-based compensation platform replaces the annual survey cycle with continuously updated benchmarks drawn from real-time payroll and equity data across 8,700+ companies and more than one million employee records (Pave Market Data Pro; VentureBeat).

How Real-Time Integration Works

Traditional compensation consulting follows a familiar rhythm: submit data once a year, wait for the vendor to clean and aggregate it, receive a report months later. By the time you act on the numbers, the market has moved.

Pave takes a fundamentally different approach. The platform maintains persistent API connections to HRIS, ATS, and equity management systems — pulling compensation data continuously rather than through manual annual submissions (Pave Market Data Methodology). This means benchmarks update in near-real-time as companies onboard employees, adjust salaries, and grant equity.

The data network skews toward technology and venture-backed companies, which is both a strength and a limitation. For tech and mid-market firms, the coverage depth is substantial. Pave's methodology page details how the platform normalizes job levels and titles across its network to ensure apples-to-apples comparisons, even when companies use different internal frameworks (Pave Market Data Methodology).

PaveOS: From Benchmarks to Full Compensation Operating System

At Total Rewards Live 2025, Pave launched PaveOS — an end-to-end compensation operating system that extends well beyond benchmarking into band design, merit cycles, and total rewards communication (Pave Blog — Total Rewards Live 2025). Three features stand out:

AI-powered Auto-Smoothing. Compensation data is inherently noisy — small sample sizes at specific levels or geographies create volatile swings. Pave's Auto-Smoothing uses AI to fill data gaps, remove statistical "rollercoasters," and automatically update pay ranges as new data flows in (Pave Q4 2025 Product Releases).

Smart Flags and Priority Talent Review. Pave AI now surfaces employees who fall outside their compensation band or are at retention risk, flagging them for manager review. Priority Talent Review lets comp teams triage flagged employees directly within the merit cycle workflow (Pave Q4 2025 Product Releases).

200+ job families, including 5 AI/ML roles. As of January 2026, Pave's benchmarking taxonomy covers more than 200 job families with dedicated classifications for AI/ML Engineer, ML Research Scientist, AI Product Manager, Data Scientist (ML), and Applied AI Engineer — critical for organizations competing in the AI talent market (Pave Blog — Total Rewards Live 2025).

Pave vs. the Legacy Players: An Honest Comparison

Factor	Radford / Mercer	Pave
Data freshness	6–12 months stale; annual survey cycle	Continuously updated via persistent integrations
Price	Six-figure annual engagements typical	Starts at $799/month; free tier for startups with 1–200 employees
Coverage breadth	Deep across industries, geographies, and enterprise segments	Strongest in tech/venture-backed; expanding into broader mid-market
Setup	Months of consulting, manual data submission	Direct HRIS/ATS integration; days to weeks
AI/ML role granularity	General "Data Science" buckets	5 distinct AI/ML role classifications

Sources: Ravio — Best Salary Benchmarking Tools; Ravio — Radford Alternatives

Competing platforms include Ravio and Figures (strong in European markets), Compensia (executive comp), and Salary.com (broad but survey-based). Each has trade-offs, but Pave's real-time integration model and aggressive pricing represent the sharpest departure from the legacy consulting playbook.

Who Pave Is Best For — and Where It Falls Short

Ideal fit: Tech companies, venture-backed startups, and mid-market firms that need fast, affordable benchmarking with strong equity compensation data. The free tier for companies with 1–200 employees makes it an easy entry point for early-stage teams. Paid plans starting at $799/month are a fraction of what a Radford or Mercer engagement costs (SelectHub — Pave).

Funding and trajectory: Pave has raised $175 million in total funding, including a Series C that valued the company at $1.6 billion. Investors include Andreessen Horowitz, Y Combinator, and Seer Capital (VentureBeat).

Limitations to consider:

Tech-network bias. The 8,700-company network leans heavily toward technology and VC-backed firms. If you are benchmarking roles in manufacturing, healthcare, or government, coverage may be thin.
Geographic gaps. Strongest in the US; international data is growing but not yet on par with global consultancies like Mercer.
Enterprise review volume. Gartner Peer Insights lists Pave with a limited number of enterprise-scale reviews, making it harder to assess performance at large-org scale (Gartner Reviews — Pave).
Dependency on network effects. The accuracy of real-time benchmarks scales with the number of integrated companies. In niche roles or geographies, smaller sample sizes mean wider confidence intervals.

The Bottom Line

Annual compensation surveys made sense when labor markets moved slowly. They don't anymore. For HR leaders at tech and mid-market companies, Pave offers a credible path to real-time compensation intelligence at roughly 1% of legacy consulting costs — with the platform depth (PaveOS) to run the full comp cycle, not just the benchmarking step.

The honest trade-off: if your workforce spans industries or geographies where Pave's network is thin, you may still need supplemental data from traditional sources. But as the network grows and AI-powered features like Auto-Smoothing mature, the gap between real-time and survey-based benchmarking will only widen.

Start with the free tier. Connect your HRIS. See how your data compares. The worst outcome is confirming what your annual survey already told you — nine months ago.