Compensation Analysis Systems

Today we are releasing our(Stello Inc.) AI Compensation Agent - purpose-built to perform complex compensation analysis and simulations in real time. Here’s the problem we're solving: Every compensation cycle, HR and Finance teams are forced into the same workflow: → Export data into Excel → Build fragile models to simulate merit and market adjustments and bonus calculations → Run scenario after scenario manually → Recalculate everything when one assumption changes Answering seemingly simple questions becomes computationally expensive: → What is the total cost of moving all engineers to midpoint? → Should high performers receive 6.0% or 5.5%… and what does that do to budget burn? → What market adjustment pool is required to bring employees to minimum? → What are the trade-offs between compa-ratio increases and retention? These are multi-variable optimization problems across workforce data, market benchmarks, performance signals, and financial constraints. Here’s what the AI Compensation Agent Does The Stello AI Compensation Agent performs this analysis instantly by combining: → Building a merit matrix under budget constraints → Scenario simulation across thousands of variables simultaneously → Explainable outputs so leaders understand why a recommendation exists → Real-time compa-ratio modeling Instead of static models, customers now operate a live compensation decision engine. Built for Computational Depth Our agent is powered by Anthropic's Claude Sonnet 4.5 - one of the fastest LLM architectures available for structured reasoning and complex calculations. This allows us to: → Execute compensation simulations across entire workforces in seconds → Maintain numerical precision across chained calculations → Move beyond generative AI → into analytical AI for enterprise decision-making The Impact for Customers Organizations using the AI Compensation Agent are now able to: → Reduce compensation planning cycles by 80%+ → Replace brittle Excel models with governed, repeatable analysis → Explore scenarios they previously avoided due to modeling complexity This is not automation. This is augmentation of compensation science. Compensation has always been one of the most data-dense, calculation-heavy processes in HR. Now it finally has infrastructure designed for that reality. *** Want to see how the AI Compensation Agent works for your next comp cycle? Book 20 minutes and I'll walk you through it: https://lnkd.in/eezQ9T73

Here’s 2-weeks of a Head of People Ops’ Claude use cases from simple to sophisticated. Level 1: Internal communication with Claude Chat (Sonnet 4.6) I launched a company-wide product training initiative and needed a Slack announcement for our #all-headlines channel. The program, Creator Quest-ions, is adventure-themed, informal, and energetic. I gave Claude the context and tone I wanted as well as the Notion doc with content for the first session. Claude drafted the announcement, made decisions about what to include vs. save for the channel itself, and linked to the project-specific Slack channel by ID so it’d render as clickable. I edited maybe two sentences. Level 2: Document redaction with Claude Cowork (Opus 4.6, extended thinking enabled) In HR, we have a lot of documents that have details with varying levels of sensitivity. If you have a PDF document and you need to remove one section (e.g., compensation expectations) before sharing it more broadly, you can have Claude Cowork redact the document with a Python script. No, you don’t need to know Python. Just describe what you want removed in plain text. No more using Preview to put black or white shape boxes over things. Level 3: Compensation analysis with Claude Cowork (Opus 4.6, extended thinking, HR plug-in) This is where Cowork gets interesting. Anthropic launched an HR plugin that’s designed for people operations — recruiting, onboarding, performance reviews, compensation analysis. It comes pre-loaded with slash commands like /offer-letter, /onboarding-plan, and /comp-analysis. You can configure it with your company’s actual benefits, equity structure, and comp philosophy so the outputs aren’t generic. I’ve been using the compensation analysis workflows alongside our existing job architecture, salary tier system, etc. to do things like calculate formulas for new sub-tiers faster (Claude can derive the quartile spread formula we use and apply it in new rows from a sheet with values only) and sanity check the data pulled from our compensation tool. Level 4: Manager updates with Claude Cowork and connected tools (Slack, GoogleDrive, Notion) We recently confirmed our outstanding performance grant awardees and I needed to individually inform managers and give them all the details along with a script and deadline to tell their direct reports. By giving Claude Cowork the GoogleSheet with the confirmed awardees and grants, the Notion SOP, and an example from last cycle, Claude could draft and send all the necessary DMs via Slack. It’s like a new version of a mail merge. Now since this is sensitive info, I did do additional double checking to confirm Claude had the right Slack IDs, manager matches, etc. I also sent a test first. Hopefully this helps make some applications of AI more concrete and exciting.

One of the most important and tricky things for startups to get right is compensation and payroll spending—especially now, with profitability under sharper focus. Yet, clear benchmarks are often missing, leading to tough and sometimes sub-optimal decisions. The overwhelming response to our report last year on Startup Compensation benchmarking encouraged us at xto10x to publish another edition of the report that includes deeper insights and more meaningful data and benchmarks for one of the most critical cost areas in any startup’s P&L. The report themed ‘Evolving Compensation Trends- Indian Startup Ecosystem’ attempts to answer some of the common questions that founders and HR leaders repeatedly ask: - What’s the right mix of cash and equity for different roles? - How should ESOPs be structured to attract and retain talent? - How does compensation evolve as a startup grows? The report includes Compensation trends and Insights on how companies across different industries & stages have approached payroll as percentage of revenue; Fixed hikes, Variable payouts and ESOP grants in 2024-25; Levelling architectures for 8 diverse job families (Marketing, Sales, HR, Finance, Product Management Data Science, Engineering & Design) and a detailed analysis of all the compensation levers - Fixed, Variable, and ESOPs. This year, we’ve expanded the scope with deeper insights on founder and leadership compensation, more business functions, and data from more startups across sectors like Fintech, Edtech, SaaS, D2C, and more. I would like to thank all the startups which contributed to the benchmarking study. Hope this serves as a useful resource for startup teams, CHROs, and investors navigating these decisions to build a competitive yet sustainable rewards framework. Would love to hear thoughts from those who’ve been working through these challenges firsthand. The free version of the report has been provided below. For the complete report or a discussion on Total Rewards, feel free to write to us at rewards@xto10x.com. xto10x PeopleCues

Compensation Grades in Workday What’s Actually Changed? Most HR teams still treat compensation grades like a static table in Workday. But in 2025, grades have quietly become one of the most powerful levers for fair, global, compliant pay. If your company is hiring across countries and compensation feels…messy, the issue isn’t your managers. It’s your grade architecture. Let me break down what’s changed ⬇️ 1️⃣ Grades are no longer just ranges...they’re the backbone of job architecture ->Old way: -Create a range → assign it to a job → hope it works. ->New way: -Build a global job architecture, then anchor each level to a grade: -Job families -Clear levels (Analyst → Manager → Director → VP) -Competencies & skills -A unified global grade per level -1 role → 1 level → 1 grade. That’s how you create internal equity at scale. 2️⃣ Localization happens in Grade Profiles, not new grades Instead of cloning grades for every new country, smart teams now: -Keep the global grade structure -Localize using Grade Profiles: -Currency -Market variations -Statutory minimums -Full-time vs part-time -Regional rules This keeps things clean....Fast... Auditable. 3️⃣ Workday 2024–2025 upgrades make grade integrity stronger ->A few game-changers: -Propose Compensation Hire embedded in the new hire UI -Range visibility + templates at the point of offer -New compliance features (minimum wage logic, red/yellow/green alerts) -Modern comp review grids that handle huge populations without breaking -Better auditability for out-of-range workers -Less spreadsheet chaos. -More manager-proof compensation. 4️⃣ Market data is finally integrated like it should be This is the biggest shift. ->Companies now combine: -Survey data -Workday benchmark data -Third-party real-time compensation intelligence (yes, live offer data) -Making min/mid/max ranges living, breathing, market-aligned inputs...not static PDFs from last year. This is vital when expanding internationally. 5️⃣ Promotions, transfers, and location moves run off grade rules In 2025, you don’t fix pay after a promotion. ->Workday recalculates automatically: -Eligibility -Plans -Ranges -Localized profiles -Compliance rules -Red/green-circle alerts Your compensation model becomes self-cleaning. If you’re scaling internationally, this stuff is not optional anymore. Want to learn how to build systems like this inside Workday? Two ways I can help 👇 1️⃣ Workday HCM 40-Hour Self-Learning Program For busy professionals who want project-style training at their own pace. Core HCM → Staffing → Compensation → Recruiting → Reporting → Integrations and more.... 2️⃣ Interview Prep Accelerator (Beta Cohort) Designed for people who finished a Workday course but still struggle in interviews. We deep-dive scenarios, confidence, storytelling & real configuration logic. Comment "workday" or DM me and I’ll send you the details. Image Src: Philippe Lauret on LinkedIn

Are you a Recruiter or HRBP trying to understand whether compensation is impacting hiring outcomes? Do you know where signing bonuses are driving acceptance rates—and where they aren't? Historically, answering those questions required stitching together data from multiple systems, often leaving you with an incomplete or outdated picture. These questions used to require pulling data from multiple systems and hoping the picture held together by the time you needed to act on it. We connected our Greenhouse data to @Rippling and asked Rippling AI to build a dashboard showing offer acceptance rates, compa ratios, and signing bonus trends across departments, job levels, and IC versus manager tracks using this prompt: "Build me a dashboard showing the cost and competitiveness of acquiring new talent — compa ratios, signing bonus trends, and offer volume — cut by org, region, and IC vs. Manager. Highlight any correlation between offer accept rates and compensation trends." A couple things became immediately clearer. 1. Overall acceptance rate was strong, but one job level was the exception. The dashboard flagged it immediately and showed that the single rejection at that level came in above the accepted average on comp, pointing to role fit or candidate experience rather than a compensation problem. That distinction matters. Throwing money at a fit issue doesn't fix it. 2. Some roles were landing below market midpoint on compa ratio while others were at or above it. Whether that reflects tighter comp bands or anchored offers in a competitive market is worth a closer look before the next hiring wave. Before this, correlating offer outcomes with compensation data meant manual work that usually happened after the fact. Now it's in one place, updated in real time, and available to any HR business partner who needs it before making an offer decision. #RipplingAI for the win! 💫 #HiringAnalytics #TalentIntelligence

The Merit Matrix Illusion: High Performers Running in Place Many organizations believe they have a merit pay program. In reality, what they have is a cost-of-living adjustment with a performance label. Here is an example. The organization uses a traditional five-point performance scale and a merit matrix tied to compa-ratio ranges. Each year, the U.S. salary structure is aged by the same percentage as the merit pool which is typically 2–4%. On paper, the design looks reasonable. But six years into the model, a troubling pattern has emerged: a large population of long-tenured employees are effectively paying a “loyalty tax.” These are solid, experienced employees who consistently receive Exceeds Expectations ratings. Yet many of them remain below market even after years of merit increases. Why? Because the salary structure moves at the same pace as their increases. The result: strong performers are running in place. This is one of the most common structural issues in merit programs. When merit budgets are small and differentiation within the matrix is limited, the program stops functioning as a reward for performance and starts functioning as a “market maintenance mechanism.” Over time, several risks emerge: • Long-tenured employees fall behind external market rates • Pay compression increases • High performers feel undervalued despite strong ratings • Retention risk rises among your most institutional-knowledge-rich employees In effect, the compensation system unintentionally penalizes loyalty. So, what can compensation leaders do? #1 - Acknowledge that merit alone cannot solve structural pay gaps. Merit budgets are designed to reward annual performance. They are not designed to correct long-term market misalignment. #2 - Separate market adjustments from merit increases. Organizations often need targeted pay equity or market adjustment budgets to address employees who have fallen behind the market despite solid performance. #3 - Examine whether your merit matrix has enough differentiation. If top performers receive only marginally higher increases than solid performers, the program will never meaningfully move people within the range. #4 - Review how your salary structure aging interacts with actual employee pay movement. If structures move at the same pace as average increases every year, many employees will remain stuck below midpoint indefinitely. A well-designed compensation strategy should reward performance, maintain market competitiveness, and reinforce retention. When those elements drift out of alignment, even well-intentioned pay programs can produce unintended consequences. Here’s a key question: Are your most loyal employees advancing or just keeping up? If your merit program is quietly creating a loyalty tax, it may be time to reexamine the design. How is your organization addressing long-tenured employees who remain below market despite strong performance? #CompensationStrategy #TotalRewards #PayEquity #HR

Here's what Range founding members are building with AI. Compensation & Benefits tools, from simple to sophisticated. Level 1: Manager training with Gemini A Rewards Manager at a music streaming platform is fully blocked on compensation data. Her Works Council requires uniform AI deployment across all offices before anything goes live. So she built Gemini gems for managers instead. One answers job level questions. Another walks through competency guidance. Neither gem touches employee data. Managers get faster answers and she gets fewer interruptions (win, win). Level 2: Multilingual benefits self serve with a custom GPT A Head of Rewards at a travel tech company built a custom GPT loaded with internal benefits documentation. It handles benefits questions across 7 countries in English, Mandarin, and Cantonese. He no longer fields basic benefits queries from employees in markets he can barely cover in person. Level 3: Salary ranges and offer approvals in 5 minutes I was skeptical at the beginning, but this is for real. A Comp Lead at an Irish tech company built a recruiter tool with Claude. You drop in a job description, and the tool parses it against Radford benchmark data, generates a salary range, pulls peer data from recent offers, and outputs an approval workflow. What took her 2 hours now takes 5 minutes. Level 4: Job classification across 8,000 job descriptions A Comp Analyst at an estimeed US university trained a Claude Opus agent on every position description in the institution's catalogue. The agent classifies new roles, flags potential bias in job descriptions, and surfaces inconsistencies across the catalogue. His team was spending roughly a third of their time on manual classification. What does a comp team do with that time back? He built this with no engineering background. Almost every practitioner I spoke to described ambitions like Level 4 while operating at Level 1. They've never seen it done. What would you build if you saw the first step? The next post in this series covers how practitioners are building pay equity and pay transparency tools. That one involves regression models and a healthy dose of nerdiness.

Most C&B consultants hand you a salary structure and call it done. I don't. Because a compensation structure isn't just numbers in a spreadsheet. It's a tool that either attracts and retains your best people, or quietly pushes them toward your competitors. After years of designing C&B structures across different markets, I've developed a framework that works. One that's rooted in reality. ↳ Phase 1: Discovery (The Foundation) Before I touch a single salary number, I need to understand three things: → What's your business strategy for the next 3–5 years? → What are the critical roles that will get you there? → What's currently broken in your compensation approach? I interview stakeholders. I review turnover data. I analyse exit interview patterns. Because if I don't know where you're going, I can't build a structure that takes you there. ↳ Phase 2: Market Intelligence (The Benchmark) This is where most consultants stop at surface-level data. I go deeper. I don't just pull generic market benchmarks. I analyze: → Your specific industry and geography → Your competitors' compensation strategies (who are you losing people to, or hiring from?) → Emerging talent trends (like equity-based incentives or hybrid work premiums) If your compensation structure isn't competitive and clearly communicated, you're already losing talent. ↳ Phase 3: Structure Design (The Architecture) Now I build. But not in isolation. I collaborate with your HR and finance teams to design: → Job grading and leveling that makes sense for your business → Salary ranges that are competitive but sustainable → Variable pay frameworks (bonuses, incentives, LTIs) that reward performance The goal is to create a structure that's fair internally and competitive externally. One that your CEO can defend and your employees can trust. ↳ Phase 4: Implementation & Communication (The Rollout) This is where most projects fail. You can have the best C&B structure in the world, but if your managers don't know how to communicate it, it falls flat. So I train your leadership team: → How to explain pay decisions transparently → How to handle difficult compensation conversations → How to use the pay structure as a retention tool, not as a cost center Because compensation isn't just about what you pay. It's about how you make people feel valued.

Most AE compensation surveys are glorified data dumps. Pages of numbers that tell you what everyone else is paying, but don't help you make better decisions about your own firm. A useful compensation data report shouldn't just show you the going rates. It should reveal the economics of running a profitable design firm and help you align compensation with performance. Here are some metrics that actually matter: 1. Direct Labor Multiplier This is your profitability barometer. If you're paying someone $80,000 and they're generating $240,000 in net revenue, that's a 3.0 multiplier. In our industry, you need 3.0+ to be healthy. Anything below 2.8 and you're subsidizing underperformers. Good data shows multipliers by position, experience level, and discipline, so you can see where your economics work and where they don't. 2. Revenue Factor Similar to multiplier but more direct: total fees billed divided by total direct compensation. This tells you who's actually profitable to employ. Your project managers should be running 3.2 to 3.5. If they're not, either their billing rates are too low, their utilization is poor, or you're paying them too much for the value they deliver. 3. Time Charged to Projects (Utilization) Compensation without context is meaningless. Someone making $120,000 who bills 75% of their time is a different investment than someone making $120,000 who bills 55%. Good reports show utilization rates by role and compensation level. You'll quickly see whether you're overpaying people who spend too much time on business development, administration, or simply not working efficiently. 4. Bonus as a Percentage of Salary This reveals your firm's performance culture. Are bonuses 3-5% token gestures or 15-25% real incentives? More importantly, good data shows the range and distribution. If your principals are getting 30% bonuses while project managers get 5%, you need to ask whether that reflects actual value creation or just ownership privilege. 5. Total Direct Compensation in Relation to the CEO Internal equity matters more than most principals want to admit. If your CEO makes 8x what your best project manager makes, you'll struggle with retention and morale. Good firms typically run 3-4x ratios between top and mid-level positions. Anything beyond 5x and you're building resentment, not loyalty. Stop collecting compensation data just to justify paying market rates. Use it to understand your firm's economics, align pay with performance, and make strategic decisions about where to invest in talent and where to make changes. The firms that win pay intelligently AND competitively, using data to drive decisions that improve both profitability and culture. That's what compensation data should actually do for you. (Plenty more metrics where these came from—comment if you want part 2)

Want to attract top talent? Start with pay transparency. 💰 Here’s what job seekers prioritize: ➤ 93% check salary details first ➤ 82% expect full compensation transparency ➤ 76% need a clear growth trajectory Yet, many companies still use outdated hiring practices: ❌ Hiding salary ranges ❌ Offering vague benefits ❌ Using meaningless terms like "competitive pay" What actually attracts the best candidates? ✔ Clearly defined salary bands ✔ A transparent breakdown of perks & benefits ✔ Clear career progression paths Today’s workforce isn’t just chasing a paycheck—they seek: ✅ Clarity in earnings ✅ Meaningful benefits ✅ A roadmap for career growth ✅ Work-life balance that’s respected Fail to provide this, and top talent will go elsewhere. Be the company that stands out. Lead with transparency. Here are some tools that can help with pay transparency, compensation planning, and salary benchmarking: Compensation & Pay Transparency Tools:: 1. Payscale – Real-time salary benchmarking & compensation insights 2. Salary.com – Compensation analysis and salary structure tool 3. Mercer | Comptryx – Competitive compensation data for global companies 4. Radford (Aon) – Pay benchmarking & workforce analytics for tech firms 5. Willis Towers Watson (WTW) Compensation Software – Salary data & workforce insights 6. Carta Total Compensation – Equity & cash compensation planning 7. FIGS (FIGO Compensation) – AI-powered salary benchmarking Compensation Planning & HR Integration:: 8. Anaplan for Compensation – Integrated workforce planning 9. Workday Compensation – Compensation planning & pay equity analysis 10. SAP SuccessFactors Compensation – Merit increases & budget management 11. ADP Compensation Management – Pay equity & total rewards planning 12. Payfactors (by Payscale) – Market pricing & internal pay equity Job Posting & Pay Transparency Compliance:: 13. Greenhouse – ATS with compensation visibility in job postings 14. Lever – Recruitment tool with salary transparency features 15. LinkedIn Salary Insights – Market pay insights in job listings Here are some relevant hashtags for pay transparency and talent attraction: #PayTransparency #FairPay #EqualPay #CompensationMatters #SalaryTransparency #TalentAttraction #FutureOfWork #HRTech #EmployerBranding #TotalRewards #WorkplaceEquity #HRBestPractices #HiringTrends #JobMarket #CareerGrowth