How Págame computes your number.
Your market value comes from a fixed formula, not an AI guess. It runs the same way for everyone, and your gender is never part of it. This page walks the whole path from your situation to your target, and names the source behind every step.
Source BLS OEWS · May 2025 estimates · Engine target-1.1.0 · deterministic, no I/O, no LLM
The VALUE framework
Five stages. You can open any of them and see what went in, what came out, and why.
ExampleOne woman’s figures, carried through all five.
You tell Págame your role, level, scope and city in your own words, and those are what it computes from. A résumé or LinkedIn PDF can accelerate your Pay Profile, but it is never required to get your number — and anything it reads is yours to correct with Rita, or to change from your profile whenever something moves. Gender never enters at all, because there is nowhere to put it.
There is no gender field to fill in.
Págame matches what you told it to the government's wage data: BLS wage curves for your role and metro. The Confidence Engine grades how solid that match is — your geography, whether your role maps to a real occupation, and how current the figures are.
Págame places your pay on that wage curve. You see your percentile, how far you sit from the median, and the range around it, read straight off the data.
Market range, worked example — your pay today: $148,000 to $164,000. Your pay today — below the 25th percentile: $126,000, below the range.
Every factor behind your result is named: role, level, location, industry. Each one arrives with a confidence read and its source. If confidence is low, Págame says so.
Your target becomes a plan: your evidence ranked by strength, and a script anchored to your number. When it is over, you tell us what happened, and it becomes part of the evidence of what asking actually gets women.
“Based on BLS wage data for product managers in Chicago, adjusted for my level, I’m asking for $174,500…”
Source O*NET 30.0 · U.S. Census Bureau (industry) · BLS OEWS. Figures are one worked example, not a claim about any reader.
One equation. Every part sourced.
BLS OEWS · May 2025 estimates
Engine target-1.1.0 · deterministic
Which is why a senior target can sit above the band’s high. The band is the market’s middle half for the role, the same for everyone in it; your level is applied to the median, afterwards.
Market range, worked example: $148,000 to $164,000. Your target: $174,500, above the range.
What the AI does — and doesn’t.
Págame uses a language model for the parts made of words: the questions it asks you, and the script you take into the room. It is never allowed to propose a number, and never allowed to lower one.
Your target comes from a fixed function that reads market percentiles and your level. The same inputs give the same answer, every time, for everyone.
Claude writes the words. It never writes the number.
Source lib/engine/target.ts · deterministic, no I/O, no LLM · stamped target-1.1.0 on every plan
What confidence means here.
It is a property of the data, never a grade on you. Lower confidence widens the band; it never moves your target.
ExampleIllustrative bands, drawn to one scale.
Wage data for your exact metro, or your state — both are large-sample and current — and your role maps cleanly to a real occupational code.
High confidence — an illustrative band: $148,000 to $164,000.
National wage data, where local markets can sit well above or below the average. The band widens, and Págame names the factor it is unsure about.
Moderate confidence — an illustrative band: $140,000 to $172,000.
Your role landed in a broad catch-all category, or the benchmark year is overdue an update. Págame says so beside the number and never presents it as a fact.
Limited confidence — an illustrative band: $128,000 to $184,000.
Source lib/engine/confidence.ts · geography tier, occupational match and benchmark year. Bands drawn to one scale.
Three questions Págame helps you answer.
ExampleEvery figure below is one worked example, not a claim about any reader.
How strong is my position?
Your target is what to ask for. Your Power Score is whether you are in a position to insist on it, and what to build first if you are not. Your score, what it means and its top drivers are free.
Strong position. Your case is built; the next move is asking.
How it is built
A fixed formula, never AI and never your gender. It weighs three factors, each yours to change: an outside alternative, such as a live offer or an active search; how far your job has grown since your pay was set; and one result you can point at.
Each can only add. Where your pay sits against the market is deliberately not one of them: being underpaid is the case for a raise, not the power to win one. You can see every factor that counts, and every score keeps a record of the method that produced it.
What could waiting cost me?
Your yearly gap, held flat over one, three and five years. It only appears when you are below your market rate.
How it is built
Arithmetic you can check: no growth assumptions and no forecast, so treat it as a floor. If your pay and the market both grow, the real cost is higher. We never invent a cost that is not in the data.
Would another role or career change the picture?
Name a job you have wondered about. See what it pays where you live, and whether your experience would carry into it. You get a plain read, never a percentage, and Págame never suggests the job.
How it is built
The pay comes off the same government wage curve as your own number, so the two are like for like. One thing is different: we compare you to the bottom of that job’s range, not the middle. Moving fields usually means starting near the bottom, so the figure errs low on purpose.
Whether your experience carries over comes from O*NET, the Labor Department’s survey of what occupations actually involve. We compare what your work leans on that most work does not, because that is the part that travels with you.
Source BLS Occupational Employment and Wage Statistics, behind your gap and what staying costs · O*NET 30.0 (U.S. Department of Labor), behind whether your experience carries over. The full method