Your comp team sits on the richest data in the company. And the slowest way to use it.

Every real question waits its turn. 

• What would a different merit split cost? 
• Are we still competitive in engineering? 
• Would this allocation open a pay gap? 

Each one queues behind an analyst, a spreadsheet, or a consultant who bills by the project.

Compensation is one of the most data-heavy corners of HR. It's also one of the least digitized. So the people paid to exercise judgment spend most of their time on groundwork instead.

CompportIQ is built to change that ratio. Here's what it is, what it lets your team do, and why it's built so you can trust what it tells you.

Speed is the easy part

Yes, CompportIQ is fast. It builds analyses, models budgets, and answers questions in minutes. But speed on its own undersells what changed.

Better compensation was never about producing a number faster. It's about understanding what that number does before it becomes a decision. Efficiency matters. It just shouldn't be the ceiling of what you expect.

The real shift is the questions you can finally afford to ask. 

• What if we differentiate more for top performers? 
• Where are we slipping below market? 
• Could this allocation create an equity risk we'll answer for later? 

Those used to be too expensive to ask mid-cycle. Now they're a sentence.

So what is CompportIQ?

CompportIQ is an intelligence layer that sits on top of the compensation platform your team already runs. Think of it as a veteran comp analyst at your side: it understands your data and context, does the groundwork, and explores the alternatives, while you stay in control.

Underneath are purpose-built agents, each built for a specific comp task. Six are live today, across pay equity, benchmarking, comp planning, budget management, calibration, and analytics. More are on the roadmap.

CompportIQ is new. The platform behind it, Compport, isn't. It's built on the same foundation that already supports 300+ customers across 37+ countries and 1.5M+ users, through millions of real comp decisions.

A generic assistant bolted onto a data warehouse doesn't know your bands, your eligibility rules, or your approval chains. CompportIQ reasons inside them from day one, because it lives where your cycle actually runs.

What becomes possible

Meet Maya. She runs Rewards for six thousand people, and she's good at it. Here's what her work looks like with an intelligence layer doing the groundwork.

Before the cycle: model the decision before you commit the budget

Salary review starts in six weeks, and Maya's hardest calls happen before a single manager enters a number. How much to spend. How hard to differentiate for performance. Whether people below market should move faster. 

Normally that's weeks of building scenarios by hand. With CompportIQ, each model takes minutes. She puts three approaches side by side within the same budget and, for each one, sees the cost, whether it strengthens pay-for-performance, how it affects market competitiveness, and whether it raises an equity concern. By end of day she has the model she wants to defend.

A merger, and two job architectures that don't match

Two businesses merge. Same jobs, different titles. Same titles, different work. 

The CHRO needs one answer: how do we bring both structures into one, and what will it cost to make pay fair? Maya loads both job architectures. CompportIQ proposes a harmonized framework with a confidence score on every mapping, then runs the equity and cost-to-remediate analysis in the same pass. She can tell the CEO what Day 1 actually costs, not just what it might.

Pay equity risks, caught early

Midway through the cycle, the question isn't whether a gap sat in last year's data. It's whether the increases on the table right now would open or widen one. CompportIQ flags it before approvals close, while there's still time to revisit the decisions that caused it.

Benchmarking on demand

A leader asks whether their team is paid competitively. Instead of commissioning a review that lands after the decision, Maya compares against licensed market data on the spot, across functions and geographies.

Every recommendation is explainable. Every assumption can be challenged. Maya stays in control of the call.

Why you can trust it with comp data

Here's the objection every comp leader has, and it's the right one: this is the most sensitive data we hold, and you want to point AI at it?

Fair question. The answer is that CompportIQ is governed by design, not by good intentions. A few things make that real.

The numbers aren't generated; they're computed. 

Every figure comes from tested engines running on your data, and the same inputs always produce the same auditable result. The language model explains the result. It never invents one.

Access follows the person, not the prompt. 

Each agent runs with the permissions of whoever's asking, enforced at the data layer. A manager sees their team, a comp lead sees the org, and the AI never widens what someone can already see.

Nothing changes a record without a named human approving it. 

The governing rule is simple: CompportIQ recommends, humans decide. Every action is logged, including the user, the timestamp, and the basis for the recommendation. And your data never trains any model. It stays tenant-isolated.

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