No Comp Review Slips the Deadline
The always-on monitor for the comp-review cycle. It detects pending reviews and budget drift, surfaces each one with its band + market + internal-equity context, and reminds the right reviewer at the autonomy level you pick — every alert audit-trailed. Read-only: it never changes pay.
HOW IT WORKS
How it keeps the comp cycle on track
From "review due" to "reviewer reminded" — using the same bands, market benchmarks, and approval chain you already enforce. It works the cycle before the comp lead has to chase anyone.
1. Detect
A merit cycle opens, an outlier surfaces, a recommendation goes stale. The loop reads the signal before the comp lead has to look.
2. Decide
Surfaces each pending review with its band, market data, and internal-equity context — read-only. Flags outliers ahead of the calibration meeting.
3. Act
Reminds the right reviewer when a review comes due, nudges when a stage stalls, and alerts the comp lead on budget drift — automatically when trust is high, queued for sign-off when the level is lower.
4. Log
Every alert, nudge, and flagged outlier lands in one audit trail tied to the review. Audit and HRBP-defensible evidence ready by default.
AUTONOMY YOU CONTROL
Three levels of autonomy. You pick.
Start with it off — it surfaces suggestions but takes no action until you say so. Move to approve for a one-tap checkpoint on every action. Let it run on its own when you're ready.
Off — manual only
Nothing fires on its own — pending reviews and budget alerts just surface on the comp console for the comp lead to act on.
Approve
It queues each reminder and budget alert for a one-tap send by the comp lead. The pending queue is your morning standup.
Auto
It sends the reminders and budget alerts automatically. Higher-impact escalations still come back to you.
Every review the loop touched gets an "AI flagged" badge
Reviews the loop surfaced carry an "AI flagged" badge with the source signals (bands, market data, equity). Reviewers nudged by the loop see an "AI nudged · 4 days waiting" tag. Outliers the loop flagged show an "AI flagged · band drift" tag before calibration.
- "AI flagged" on reviews the autopilot surfaced with context.
- "AI nudged · 4 days waiting" on reviewers waiting on action.
- "AI flagged · band drift" on calibration cases ahead of the meeting.
- Source-signal summary on every flag — which band, which market data, which equity signals.
One console — comp's home for cycle autopilot
The AI Comp Reviewer console is the buyer-facing landing for comp leads and HRBPs. Pending- recommendations count sits front and center with a per-cycle sparkline. The "AI handled" feed shows what fired across reviewers in the last day. The "Waiting on you" queue surfaces approval-gated final numbers. Band-drift radar surfaces outliers ahead of calibration.
- Hero metric + trend — pending recommendations + throughput sparkline.
- "AI handled this" feed — reminders, nudges, and flagged outliers in the last day.
- "Waiting on you" queue — approval-gated final numbers approved or rejected inline.
- Band-drift radar — outlier cases the loop flagged, surfaced before calibration.
- Autonomy dial — flip the loop from observe → suggest → approve → auto without leaving the console.
Why Pay Conversations Are So Painful
AI Comp Reviewer gives every employee a clear, grounded view of their own pay — so the conversations HR has are the ones that actually need HR, not the ones that just needed a number.
Employees Guess At Their Pay Band Placement
They know their salary. They don't know whether it sits at the 25th, 50th, or 75th percentile of their band. Without that context, every market article and Glassdoor post becomes a source of anxiety — and a ticket to HR.
Annual Review Timing Is A Surprise
The employee thinks their review is "in March." HR has them on an October cycle. By the time it comes up, the employee has been silently expecting an adjustment that wasn't coming. Calibration conversations get harder than they had to be.
Compensation History Is Scattered Across Offer Letters
To answer "how has my pay changed over time?", the employee has to dig through three years of offer letters, merit notes, and email PDFs. The data exists in the HRIS — but it's buried under a tab in a tab in a tab.
Bonus Eligibility And Targets Are Verbal Folklore
"I think I'm eligible for a 15% bonus, paid in March, but it might be quarterly?" Pay plans live in the recruiter's head and a PDF nobody re-reads. When the bonus shows up, it's a surprise — for better or worse — and that's not how compensation should work.
Total Comp Calculations Never Include The Pieces That Matter
Base salary feels like the whole number, but the real picture includes equity vesting, bonus target, 401(k) match, ESPP, and the benefits load. The employee comparing a competing offer doesn't have a clean breakdown of what they actually earn — so they undersell their own package, or worse, accept an external offer that's $30k lower in total value.
Equity Refresh Cycles Are A Black Box Until They Hit
The engineer's initial grant vests over four years. Year three rolls around and they start wondering — does a refresh come automatically, is it tied to performance, when do I find out? Without grounded answers about their own equity timeline and refresh eligibility, the best people start interviewing externally to figure out their own worth, and the company finds out too late that retention was the conversation that never happened.
AI Comp Reviewer At A Glance
AI Compensation
Market benchmarking and internal equity analysis.
Inside AI Comp Reviewer — The Actual Capabilities
Every block below maps to a real tool the agent uses against the employee's compensation record. Strictly read-only — the agent answers, it never changes a pay record. Bias-guarded and PII-masked on every LLM call.
"What Am I Paid Right Now?" — Grounded And Specific
The most common compensation question, answered without an HR ticket. The agent surfaces base salary, pay type, pay grade, range placement, and compa-ratio — so the employee knows not just the number but where it sits inside their band.
- View current compensation — base salary, hourly rate, pay type (exempt / non-exempt), pay frequency.
- View pay grade summary — grade, range minimum / midpoint / maximum, and compa-ratio when pay grade data is available.
- Range placement — where in the band the employee sits, with quartile context.
- Permission-aware — employees only see their own compensation; the agent does not expose peer comp.
How Your Pay Has Changed Over Time
Every salary change in one place, with the reason, the delta, and the direction. The employee doesn't have to dig through offer letters and merit notes to reconstruct their pay history — it's all on the record, and the agent surfaces it on demand.
- View full compensation history — every salary change since hire, with effective dates and reasons (merit, promotion, market adjustment, etc.).
- View last compensation change — the most recent change with direction (increase / decrease / lateral) and reason.
- Compare current to previous — summary of the most recent change with delta amount and percentage.
- Compensation summary — tenure, total change history count, and high-level trajectory at a glance.
Next Review Date And Bonus Eligibility
Two questions that drive most "I'm thinking about my pay" conversations — when's the next review, and what's my bonus target? Both answerable in chat, both grounded in the comp plan stored in the HRIS. No more verbal folklore.
- View next review date — when the next compensation review is scheduled, and whether it's on-cycle or off-cycle.
- View bonus eligibility — is the employee in the bonus plan, what's the target percentage, what's the payout window.
- Cycle context — annual vs quarterly, fiscal-year alignment, calibration timeline.
- Read-only — the agent surfaces dates and targets; reviews and bonus payouts happen in the Compensation app.
Outcomes Teams Can Measure
The agent's job is to make pay transparent without adding work for HR — to absorb the questions that were already on the HRIS and free HR for the conversations that require a human. Measure against your pre-agent baseline.
- HR comp-question deflection — pay, history, and review-date questions absorbed by the agent vs ticketed to HR.
- Pay transparency score — share of employees who can correctly state their pay grade placement (survey instrument, comparable across cycles).
- Review-cycle visibility — share of employees who know their next review date within 30 days of accuracy.
- Bonus understanding — share of bonus-eligible employees who know their target percentage and payout window.
- Off-cycle adjustment volume — comp inquiries that escalated to off-cycle reviews because the original question had no clear answer.
Intentionally Read-Only · Bias-Guarded · PII-Masked
AI Comp Reviewer's RISKY_TOOLS list is empty — the agent retrieves and explains, it never changes a compensation record. Bias guard masks protected-attribute terms before every LLM call; PII protection masks SSNs, addresses, and identifying numbers. The employee sees their own pay through the agent because the tool query is scoped to them, not because the LLM was handed personal data.
- Zero write tools — RISKY_TOOLS list is empty. No pay changes, no review-date moves, no bonus adjustments.
- Permission-aware — employees see only their own compensation; no peer visibility, no manager-view shortcuts.
- Bias guard applied — protected-attribute terms are masked before any LLM call.
- PII protection on every prompt — SSNs, addresses, and identifying numbers masked before reaching the model.
- Audit trail on every retrieval — every lookup logs the requesting user, the tool used, and the parameters.
WHAT TEAMS TRY INSTEAD
The four alternatives — and why none of them give THIS employee a grounded view of their own pay
HR and total-rewards teams trying to drive pay transparency usually try one of these four. None of them combine band placement, change history, bonus eligibility, and equity timeline in one PII-masked, bias-guarded conversation.
Pasting offer letters and Glassdoor pages into ChatGPT, Claude, or Copilot
Employees stitching their own comp story from scattered documents
- The agent reads the employee's actual HRIS record — band placement, change history, bonus plan, equity timeline
- Bias guard masks protected-attribute terms before any LLM call — a generic chatbot has no notion of that protection
- PII (SSN, address, identifying numbers) is masked before reaching the model
Pave AI / Lattice Compensation AI / Workday Comp Advisor
Vendor-trapped compensation AI inside one HRIS or compensation platform
- Composes with Performance, Payroll Assistant, Headcount Planner, and Offer Manager — not stuck inside one comp surface
- Available to every employee, not just HR business partners on a vendor seat
- No second per-employee AI license on top of the comp-tooling contract
A custom comp-transparency chatbot
An engineering team's six-month build, then forever maintenance of PII-handling and bias-guarding
- Shipped already. Engineering spends zero weeks plumbing band placement, change history, equity timeline, or bias controls
- Read-only on the employee's own record by design; security and HR-legal review is a one-pager
- Inherits new capabilities (richer market comparisons, new equity logic) as the platform evolves
The manual fallback — "ask your manager or HR business partner"
The default when pay questions surface
- Band-placement and next-review-date questions self-serve — HRBPs handle the calibrations that actually need them
- Bonus eligibility and equity-refresh timeline stop being verbal folklore
- Compensation history is one query away, not a three-year offer-letter archaeology project
PLATFORM LEVERAGE
AI Comp Reviewer inherits everything the platform already runs
A standalone comp bot has to plumb each of these. AI Comp Reviewer gets them for free because Performance, Payroll, Headcount, and Offer Manager already do.
Cross-app data plane
HRIS comp records, performance ratings, payroll history, and offer-letter context all reach the same agent — no separate analytics export.
Strict permission model
Employees see only their own compensation — no peer visibility, no manager-view shortcuts, no "let me check yours" path. The agent inherits the same boundary the comp app enforces.
Bias guard on every call
Protected-attribute terms are masked before any LLM call — an architectural protection, not a prompt instruction.
PII protection in the prompt
SSNs, addresses, and identifying numbers are masked before reaching the model on every call — same protection layer used across the platform's AI calls.
Audit trail on every retrieval
Every lookup logs the requesting user, the tool, and the parameters — useful for HR compliance and pay-equity audits.
RubyLLM-grounded model tiering
Comp lookups run on cheap nano/small models; multi-source reasoning across history, band, equity, and bonus uses standard tier — automatically, per call.
INDUSTRY FIT
Industries where pay transparency moves retention and trust the most
AI Comp Reviewer helps wherever a knowledge-heavy or pay-band-driven workforce is comparing internal pay to the market.
Technology
Engineering and product talent get grounded band placement and equity-refresh timeline answers instead of interviewing externally to "figure out their worth."
Financial Services
Bonus-heavy roles get clarity on plan, target, and payout timing; pay-equity audits get a defensible audit trail.
Healthcare
Clinical staff understand shift differentials, on-call premiums, and bonus eligibility without HR Q&A backlog.
Professional Services
Billable-utilization-tied bonus structures, promotion-bands, and equity programs become transparent to every consultant.
Public Sector
Step-and-grade systems become self-serve queryable inside FedRAMP-eligible deployment options with full audit logs.
Multi-Regional Enterprises
Employees across regions see their own pay band, market context, and change history without exposing peer or cohort data.
WHY MANGOAPPS WINS
An embedded agent beats a chatbot, a vendor add-on, or a custom build on every axis
The argument finance, security, HR, and total-rewards all share — and the one a single-vendor comp-platform AI structurally cannot answer.
Cheaper than the alternatives
No per-seat ChatGPT license, no Pave AI subscription, no Lattice Comp AI tier, no Workday Comp Advisor add-on, no six-month custom build.
More secure
Strictly read-only on the employee's own record. Bias guard and PII masking apply on every call — architectural, not prompt-only. Comp data stays inside the tenant boundary.
Easier to deploy
Already deployed if you have Compensation enabled. Turn the agent on against the existing HRIS comp record and it's running the same day.
Easier to use
Lives inside Ask AI — no separate comp portal, no offer-letter archaeology, no "let me check with HR" delay.
Easier to manage
Band visibility, equity-refresh policy, and bias-guard configuration all sit in the same admin console as every other app. One audit log, one access model.
Easier to extend
Shares the agentic tool framework with every other MangoApps agent. New comp slices (deferred comp, RSU refresh, geo-differentials) ship as tools, not rewrites.
AI is actually better
A vendor comp AI can show a band chart. Only AI Comp Reviewer gives the employee their band placement, change history, next review date, bonus eligibility, and equity timeline — bias-guarded and PII-masked — in one grounded conversation.
At our head office within HR, marketing, and finance, we use ChatGPT quite a bit, and MangoApps AI Helper is a useful tool for us. As I go around to the stores, I’ve been showing them how the AI Intranet Helper can find things and answer plain language questions about our resources, and they’re quite excited about that.
Alison van Zijl
Head of Human Resources
Full House
Customer Success
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Frequently Asked Questions About AI Comp Reviewer
8 tools across employee-facing compensation visibility — view current compensation (salary, pay type, rate), view full compensation history, view next review date, view compensation summary (tenure + change count), view last compensation change with reason and direction, compare current to previous compensation, view bonus eligibility and target percentage, and view pay grade summary (grade, compa-ratio, range placement).
No. RISKY_TOOLS is empty — the agent retrieves and explains, it does not change compensation records, move review dates, or modify bonus targets. Actual changes happen through HR in the Compensation app, where approvals and audit are captured directly.
No. The agent is scoped to the employee asking, about their own compensation. There are no peer-comp tools and no manager-view shortcuts for direct reports' pay. Aggregate market benchmarks and offer-comparison tools live in Offer Manager Agent — for recruiters, not employees.
Compensation data contains sensitive PII. The agent runs PII Protection — SSNs, addresses, and identifying numbers are masked before any LLM call. Bias Guard masks protected-attribute terms before any comparison or history call. The employee sees their own data because the tool's database query is scoped to them, not because the LLM was given personal data to work with.
HR comp-question deflection, pay transparency score (survey-based), review-cycle visibility, bonus understanding, and off-cycle adjustment volume. Compare against your pre-agent baseline.
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