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Methodology
Every number grounded in public data and your own history — nothing invented.
Every number in KairoMatch is grounded in public government data — BLS wage tables and the O*NET occupation database — connected to your own work history. Nothing is invented: when the data can't support a figure, the product says so instead of guessing.
Your work history is parsed into roles, skills and accomplishments. The résumé itself is yours — analyses read it; nothing edits it.
Your latest role is coded against the O*NET-SOC taxonomy (the U.S. Department of Labor's occupation database). When the match isn't confident, KairoMatch says so and asks you to confirm — an honest abstention beats a wrong wage table.
Pay benchmarks come from BLS wage tables for your occupation and metro. Skills and career standing are graded against what O*NET says your occupation expects. An LLM connects your own history to that data — it is search and glue, not the source of your numbers.
Career paths rank real occupation transitions from the same taxonomy, and job-fit scores measure a posting against the same skills evidence. One spine of data, so the views can't disagree with each other.
They are not versions of each other and they are not meant to agree — each answers a different question.
The full grading system — every score, what it grades, and what it deliberately doesn't — is public at How scoring works. The weights and cut points on that page are read live from the scorer, so it cannot show a version of the formula the product no longer runs.
Data current as of O*NET 30.1 (November 2025), with some tables from O*NET 30.3 (May 2026).
Methodology
Every number grounded in public data and your own history — nothing invented.
Every number in KairoMatch is grounded in public government data — BLS wage tables and the O*NET occupation database — connected to your own work history. Nothing is invented: when the data can't support a figure, the product says so instead of guessing.
Your work history is parsed into roles, skills and accomplishments. The résumé itself is yours — analyses read it; nothing edits it.
Your latest role is coded against the O*NET-SOC taxonomy (the U.S. Department of Labor's occupation database). When the match isn't confident, KairoMatch says so and asks you to confirm — an honest abstention beats a wrong wage table.
Pay benchmarks come from BLS wage tables for your occupation and metro. Skills and career standing are graded against what O*NET says your occupation expects. An LLM connects your own history to that data — it is search and glue, not the source of your numbers.
Career paths rank real occupation transitions from the same taxonomy, and job-fit scores measure a posting against the same skills evidence. One spine of data, so the views can't disagree with each other.
They are not versions of each other and they are not meant to agree — each answers a different question.
The full grading system — every score, what it grades, and what it deliberately doesn't — is public at How scoring works. The weights and cut points on that page are read live from the scorer, so it cannot show a version of the formula the product no longer runs.
Data current as of O*NET 30.1 (November 2025), with some tables from O*NET 30.3 (May 2026).