AnyComp
Compensation as a decision system. AnyComp turns pay from a spreadsheet that forks every cycle into a versioned, market-anchored decision service — and opens the same model to the public. Three faces on one substrate: a public job-and-pay browser (profiles, leveling, OEWS/SOC-anchored comp stats); the enterprise Compensation Decision OS (you can't price pay job-by-job — it runs scenarios across all your jobs and people at once, on a replayable cycle audit); and a personalized path for individuals to grow their pay through transferable roles and targeted skill-building.
Compensation can't honestly be done one job at a time: ranges, equity, and budget interact, so a single-role view is always incomplete. Yet merit lives in spreadsheets that fork every cycle — formulas vanish, approvals leave no record, and 'what changed vs plan' becomes forensic archaeology. On the other side of the same data, individuals have no clear, grounded path to raise their own pay.
Live today as the toolbox Compensation Decision OS (PAT-AC1): versioned comp models (levels, geography, midpoint curves), market-anchored band math against OEWS/SOC labor data, merit / variable-pay / equity / discretionary engines, and a strategy → priorities → objective → optimizer → simulator → scenarios decision loop that returns several scored options ('never one option') on a replayable cycle-audit trail. The standalone public product — a job-and-pay browser plus a personalized 'grow your pay' pathway — is in active build on the same substrate.
- 01One substrate, three faces. The same market-anchored comp engine powers a public job-and-pay browser (acquisition), the enterprise Decision OS (the holistic, scenario-based core), and a consumer 'increase your pay' pathway — each a different door into one model. The public single-job view itself demonstrates why real comp needs the portfolio view.
- 02Government-grade data, not crowd-sourced guesses. Pay figures anchor to OEWS/SOC labor statistics and a transparent methodology, so the public browser stands on defensible data rather than self-reported salary rumors — the differentiator in a crowded salary-site space.
- 03'Increase your pay' is a learning system, not a tip sheet. It sets a goal-role, finds transferable roles that share your skills and pay more, and sequences the specific skill-building to close the gap — the career-facing instance of the portfolio's adaptive next-best-step engine.
- 04Decisions, never one option. The enterprise loop returns several scored scenarios across multiple value measures with a full audit trail — comp as a versioned, replayable service, not a lost workbook tab.
Early — the Compensation Decision OS is live as a toolbox spoke; the standalone public product and its commercialization are in active build, with strong monetization characteristics on both the public and enterprise sides.
AnyComp started from a real truth: you can't set pay job-by-job, because ranges, equity, and budget all interact — so it became a Compensation Decision OS that runs scenarios across the whole org. Opened to the public, that same market-anchored substrate becomes a job-and-pay browser and a personalized path to grow your pay — the consumer face of the portfolio's jobs-and-skills engine. One model, three doors: look it up, decide it across your org, or use it to earn more.