Pay Analytics & Compensation Intelligence
AI-driven Compensation Analytics to power fair, competitive pay insight & decisions
Why Compensation Analytics?
Traditional compensation benchmarking is costly, inconsistent, and difficult to access. INOP provides real-time salary intelligence and compensation analytics to ensure fair and competitive pay decisions.
Pay Analytics Built for HR teams, business leaders, and compensation analysts ready to move beyond spreadsheets. Make every pay decision smart, equitable, and regulation-ready — powered by real-time global data and AI-driven insights
Confident Pay Analytics Starts with Better Data
Get global salary insights, benchmark with ease, and build a fair, transparent pay analytics and strategy that scales—compliant, competitive, and trusted by your team.
Access real-time global salary data across 16 countries, industries, and company sizes.
Compare against market competitors and make informed about any pay decisions
Spot pay gaps, ensure fairness, and stay compliant with emerging regulations like the EU Pay Transparency Directive
AI-Powered Compensation Insights
We leverage 3.5M+ global job postings, acquired compensation datasets, and survey data. Our AI-driven predictive salary intelligence ensures accurate, actionable insights
Future-Ready with Skills-Based Compensation
Modern pay strategies are moving away from rigid job titles toward skills-based compensation — where employees are rewarded for what they can do, not just what their job says they do.
INOP enables skills-based pay frameworks by aligning compensation to real-world competencies — making salary decisions more fair, dynamic, and legally defensible.
This approach not only supports compliance with the EU Pay Transparency Directive, but also strengthens your organization’s reporting readiness under the Corporate Sustainability Reporting Directive (CSRD) — where fair pay, equity, and workforce transparency are key focus areas.
Be Regulation Ready by 2026
With the EU Pay Transparency Directive coming into effect, companies with 100+ employees will need to:
Report and explain gender pay gaps over 5%
Share pay analytics & criteria with employees
Ensure objective, skill-based pay structures
Use Case
Client: Confidential (German-based tech company, operating globally)
A growing, innovation-led company in the tech space required a clear, real-time evidence-based view on compensation positioning for a set of highly specialized leadership and senior technical roles. INOP partnered closely with the client’s trusted advisor and the company’s CEO to deliver a compensation benchmarking analysis that combined sector-specific insight with broader cross-sector and geographic market perspectives.
The work included transparent role mapping, robust statistical outputs, and clear guidance to support executive-level decision-making around scarce and critical leadership roles in a highly competitive talent market.
Testimonial
FAQ
We collect salary and compensation data from multiple complementary sources: job postings, job boards, career platforms, and salary platforms across industries and geographies, including user-reported salaries and employer-provided information; published surveys from leading consulting and recruitment firms; official labour department data from
multiple countries; and acquired datasets from third-party compensation data providers.
Where data gaps exist, we apply Generative AI techniques to model and infer missing values. These AI-estimated figures are clearly flagged within the platform and are used only to supplement, never replace, directly observed data. Our database architecture is also built to scale seamlessly with data from external parties, including third-party survey providers.
Update frequency is calibrated to each source type. Job postings and job boards are harvested on a continuous, daily basis. Career platforms are refreshed quarterly, in line with their own update cycles. All other sources, including surveys, official labour data, and third- party datasets, are updated as new data becomes available. This layered approach ensures that our benchmarks reflect the market as it is today, not six months ago.
We currently hold rich datasets across Western Europe, including France, Germany, the Netherlands, Belgium, Portugal, Greece, Sweden, Denmark, and Romania; the British Isles, covering the United Kingdom and Ireland; North America, including the United States and Canada; and Asia, covering the Philippines and India. We cover all industries and job levels
from junior through to senior. Executive-level compensation is included, though data is naturally less dense at this level given that senior roles are less frequently advertised publicly.
Our platform is underpinned by proprietary taxonomies: a Jobs Taxonomy of 2,400 roles with detailed descriptions derived from millions of job postings, a Skills Taxonomy of 22,700 unique skills, and additional taxonomies covering industries, sectors, values, and interests.
For geographies outside our standard coverage, we operate a co-creation model with clients; once priority regions are identified, we harvest and enrich the relevant data on a
client-specific basis
Where is data stored, and is EU residency guaranteed?
Our primary data storage is in Frankfurt, Germany (AWS), ensuring full EU data compliance by default. We also support multi-region storage for clients with specific business or regulatory requirements, and for those needing complete data sovereignty, on-premises or private cloud deployment is available. All data is encrypted in transit and at rest.
What are your security certifications and compliance standards?
We are built to enterprise-grade security standards, certified under ISO 27001 (Information Security Management), ISO 27017 (Cloud Security), and SOC 2 Type II, and adhere to GDPR and CCPA. Our AI platform is aligned with the transparency and fairness principles of the EU AI Act: all models are fully documented with human-readable explanations of how outputs are generated; performance is continuously monitored and audited for fairness and accountability; and AI is decision-supportive throughout, with final decisions always made by human stakeholders
multiple countries; and acquired datasets from third-party compensation data providers. Where data gaps exist, we apply Generative AI techniques to model and infer missing values. These AI-estimated figures are clearly flagged within the platform and are used only to supplement, never replace, directly observed data. Our database architecture is also built to scale seamlessly with data from external parties, including third-party survey providers.
Location is a core dimension of every benchmark. You can view compensation data for a specific city or region and compare it directly against other locations side by side, for example, the same role benchmarked across Amsterdam, London, and Berlin simultaneously. This supports informed decisions on location-based pay strategies, international hiring, and geographic compensation differentials.
Yes. Within the compensation module, you can build and save a defined list of competitor organisations. We will then automatically surface whether those competitors are actively hiring, or have recently posted a vacancy, for the exact role you are benchmarking. This
gives your HR and reward teams a live read on where competitor demand is building, so you can act on compensation decisions before talent pressure affects your ability to hire or retain.
Every benchmark goes beyond a pay figure. For each role, the platform surfaces a detailed role description, required skills, and other role-level requirements, giving you full transparency into what is being captured in the benchmark. You can also see which skills associated with that role are currently in demand in the market, ranked by demand level. This helps you understand not just what a role pays, but which competencies are driving that compensation and where the market is headed.
How can we access or integrate INOP's data?
We support a range of access and integration methods to suit different technical environments: a RESTful API, file exports in CSV, Excel (XLSX), and SQL formats,
embedded iFrame content for in-platform use, and FTP file sharing. Full API documentation is available at docs.inop.ai/api reference/introduction. For details on our technical stack, security setup, and architecture, please contact us directly
What is a Confidence Score and how do I read it?
Every compensation benchmark in our platform includes a Confidence Score: a dynamic, objective indicator of how reliable and representative that benchmark is. It is calculated based on four weighted factors. Where verified data is supplemented by AI modelling, this is clearly indicated within the platform.