Wizards · the Self-Serve tier

See what getting it right is worth — in two minutes, before you buy anything.

Each wizard is a short, guided diagnostic. Answer a few questions and we put a value-of-informationestimate on the table — what the blind spot is likely costing you, with honest error bars. Then we hand you the self-serve pack that does the work yourself, or the concierge product if it’s a whole-org job. No quotes, no gate.

3 diagnostics · grounded in 3 live spokes · value-of-information, never cost · error bars on every number

Run a diagnosticBrowse the packs

How a wizard works

The estimate comes first. The pitch comes after.

We don’t make you sit through a form to earn a number. The diagnostic shows its value-of-information estimate the moment you land on it, updates as you type, and only then points you at the tool. We price for the funnel, not the cost — and we’d rather show you the stake than sell you on faith.

  1. 1

    Answer a few questions

    Rough figures are fine — we band every estimate rather than pretend at precision you don't have yet.

  2. 2

    See the value-of-information

    What getting this right is worth, shown with low/high error bars and the math behind it — never a point estimate, never a cost framing.

  3. 3

    Take the matching tool

    Self-serve drop-in pack for Sheets · Excel · Power BI · Tableau — or hand it to a concierge run when it's a whole-org job.

The diagnostics

3 questions worth two minutes.

Each is grounded in a real toolbox spoke — the same engine the concierge products run on. Jump in anywhere.

CompensationConcierge

Is your merit grid quietly outrunning your pay structure?

Find the payroll creep your merit budget creates when midpoints don't keep pace.

You give solid merit increases every year, but you can't see how much of that spend is just lifting people up their range with nothing to show for it.

Two-minute diagnostic

Answer 3. The estimate updates as you type.

A rough order of magnitude is fine — we band the answer, not pretend at precision.

$

The average raise % that lands in people's base pay each cycle.

%

Midpoint / range advancement. Many orgs freeze this in lean years — which is exactly where creep hides.

%

Nothing you type leaves your browser — this runs entirely on this page.

What getting this right is worth

$619K / yr
est. range$402K – $835K
lowpoint est.high

That's the value of seeing your creep before it compounds — payroll moving up the range each year with no retention or performance bought.

  • Merit (3.5%) vs structure (1.0%) → ~2.48%/yr of compounding compa-ratio drift.
  • At this merit grid, a frozen-midpoint salary doubles in ~20 years — the mechanism behind silent creep.
  • Screening estimate off two inputs, banded ±35%. The concierge run reads your actual range placement, mix, and tenure to replace the band with a real number.
  • Honest caveat: not all creep is waste — long tenure and few new hires explain some of it. AnyComp separates structural creep from earned position.

Grounded in the anycomp spoke — the merit-vs-structure compounding rule (Milkovich/Gerhart/Newman) the anycomp spoke uses to flag managerial compa-ratio creep.

Graduate to Concierge

See it on your data — talk to us about AnyComp→

Compa-ratio drift across a whole population is a Concierge job — we run AnyComp on your data org-wide. (We never auto-promote a black box; you see the merit-vs-structure math.)

Read how AnyComp decides→

The decision layer behind the number: strategy → priorities → optimizer → scenarios.

OrganizationSelf-Serve · Hierarchy Abstractor

How many management layers is your headcount really carrying?

Compare your org's layers and average span to what your headcount implies — spot the bloat.

Decisions feel slow and management feels heavy, but you can't say whether you have too many layers or just normal complexity.

Two-minute diagnostic

Answer 3. The estimate updates as you type.

CEO = layer 1, their reports = 2, and so on down to a front-line IC.

Base + benefits + overhead. We band it; a rough figure is fine.

$

Nothing you type leaves your browser — this runs entirely on this page.

What getting this right is worth

$42M / yr
est. range$25M – $59M
lowpoint est.high

That's the management overhead your extra layers carry — and the value of mapping where they sit before a reorg guesses.

  • Implied average span ≈ 2.8 reports/manager. A span-6 org this size would run ~5 layers; you reported 8.
  • ≈ 3 excess layers → roughly 236 more management slots than a span-6 structure needs.
  • Screening estimate from three inputs, banded ±40%. Averages hide outliers — the pack joins layer, span, and depth onto every row so you find the actual thin-span pockets.
  • Honest caveat: some functions legitimately run flatter or deeper. This flags where to look, not who to cut.

Grounded in the org-graph spoke — the layer / span-of-control / depth abstraction the org-graph spoke computes from a manager-chain employee list.

Do it yourself — Self-Serve

See the Hierarchy Abstractor pack→

Self-serve drop-in for Sheets · Excel · Power BI · Tableau: paste your employee list, get layer / span / depth on every row — source untouched. Built on the org-graph spoke; no reorg consultant required.

Or have us run the org analysis→

Elective. The pack does this yourself; concierge is insurance, not dependency.

SegmentationSelf-Serve · Segmentation Adjuster

Are your people metrics reported on the wrong cut?

Find out how many decisions you're making on raw HRIS codes instead of the segments you actually manage.

Your dashboards group by whatever department codes the HRIS happens to carry — not by the four functions you actually run the business by — so trends average out and the real story hides.

Two-minute diagnostic

Answer 3. The estimate updates as you type.

The raw values your reports group by today.

The functions / cuts leaders actually make decisions on (e.g. 4 functions).

Headcount moves, pay actions, program rollouts, attrition reviews — anything you slice by segment.

Nothing you type leaves your browser — this runs entirely on this page.

What getting this right is worth

~14 decisions / yr
est. range~8 – ~21
lowpoint est.high

That's how many decisions a year may rest on a cut that averages your real segments together — the value of reporting on the segmentation you actually manage.

  • 32 raw codes collapsing into 5 real segments ≈ 6.4 codes per segment — that aggregation is where reversals (a trend that's fine overall but bad in every segment) hide.
  • Estimated 23% of your segmented decisions are exposed to the wrong cut.
  • Screening estimate from three inputs, banded −40% / +50%. The real exposure depends on how concentrated your population is — the pack lets you recode and see it directly.
  • Honest caveat: this is a directional risk signal, not a finding. It tells you to recode and check, not that any specific decision was wrong.

Grounded in the segmentation-studio spoke — the canonical-field recoding the segmentation-studio spoke does — raw source columns → the segmentation you report by.

Do it yourself — Self-Serve

See the Segmentation Adjuster pack→

Self-serve drop-in: maintain a simple mapping (raw codes → the segments you mean) and get an adjusted-segment column to report by — a view layer, source data untouched, re-runnable. Built on the segmentation-studio spoke.

Or have us normalize your HRIS→

Elective. The pack handles the common case yourself; concierge is for messy multi-source HRIS.

Most of this, you can do yourself.

The wizards point you at self-serve packs because that’s the honest answer most of the time — drop-in capabilities for the tools you already use. Concierge is for when it’s a whole-org job or you’d rather have it run for you — not the default. Support is elective insurance, never a dependency.

Browse the packsTalk to us about a concierge run