How Orune computes what it shows you.
A plain-language guide to what the system measures, where the data comes from, and what it cannot know.
What Orune Optimizes For
Education.
Every output is framed to build your understanding of what is changing in your role and your field. Orune teaches the landscape so you can read it on your own.
Calibration.
Orune compares your self-assessment against external data so you can notice where your sense of things and the public record diverge. Calibration over confidence.
Honesty.
Orune surfaces uncertainty. When confidence is low, it says so. When data is missing, it says so. The product gets quieter when there is nothing to add.
What Orune Does Not Do
It does not recommend.
You will not see imperative language like “you should.” Orune frames moves as options worth considering. The choice belongs to you.
It does not predict your specific outcome.
Scores reflect general patterns from public data. They are not a forecast for any individual employer, market, or career. “Our analysis suggests” is the strongest claim you will see.
It does not sell your data.
Your context stays in your account. Orune does not sell, rent, or share individual user data. Aggregate, anonymized patterns may inform improvements to the product, never anything that identifies you.
It does not pretend to know your circumstances.
Orune knows what you tell it and what public data says about your role. It does not know your manager, your pipeline, your savings, or the specifics of your situation unless you choose to share them.
How The AI Exposure Score Works
When you check a role, Orune looks at the task structure of that occupation. The U.S. Department of Labor maintains a public database called O*NET that describes what people in each occupation actually do. Orune uses this as the starting point.
Each occupation in O*NET is broken down into tasks. Orune classifies those tasks across five dimensions: cognitive complexity, routine content, interpersonal requirements, physical components, and creative demands.
The score weights those dimensions to reflect how current AI tools tend to interact with each. Routine and cognitive task share lift the score, because those are the categories most affected by AI today. Interpersonal, creative, and physical components reduce the score, because those tend to be areas where AI is more limited or where regulation, embodiment, or human judgment matter most.
The output is one of four bands: lower, moderate, elevated, or higher exposure. The band describes how much of the role overlaps with what AI tools can do today. It is not a prediction about whether your specific job will change.
How The Brief Works
Your brief combines your occupation’s task data with context you provide. Years of experience, employer size, and employer AI maturity all shift the picture. So does anything you choose to add through the deeper questions.
From those inputs, Orune computes your position across four dimensions: Foundation, Position, Growth and Readiness, and Life Design. Each dimension reflects a different kind of resilience. The brief shows where you are now and how that picture might shift over time if nothing else changes.
The brief grows as you engage with it. With less context, the brief stays general. With more context, it gets specific. You control the depth.
What “Benchmark” Means
A benchmark in Orune is an independent estimate. It is computed from external data such as BLS wages and employment projections, along with O*NET task patterns, rather than from your own self-assessment.
The benchmark sits next to your self-assessed score so you can see where the two diverge. A gap is not a verdict on either number. It is a starting point for noticing how your sense of your position compares with what public data suggests for professionals in similar situations.
When the gap is large, Orune normalizes it: most people over-estimate or under-estimate at least one factor. The point is calibration, not correction.
Data Sources
O*NET (U.S. Department of Labor)
The public occupational database that describes what people do in roughly 1,000 occupations. Orune uses O*NET task structures and dimension scores as the starting point for every analysis.
BLS OEWS (Occupational Employment and Wage Statistics)
Annual wage and employment data from the U.S. Bureau of Labor Statistics. Orune uses median wages and percentile ranges to ground financial benchmarks for each occupation.
BLS Employment Projections
The 10-year employment growth projections published by BLS. Orune uses these to estimate demand elasticity and to inform whether productivity tools have historically expanded or contracted occupations like yours.
How The System Measures Itself
A system that talks about your future should be willing to check its own work. Orune does this in three ways.
Scenario calibration. Orune tracks scenarios about how the AI economic transition unfolds and updates probabilities as signposts arrive. Over time the calibration of those probabilities can be measured against what actually happens. Better calibration, not louder certainty, is the goal.
Compliance audits. Every piece of generated text passes through a compliance gate before it reaches you. The gate enforces educational framing, blocks investment or directive language, and logs violations so the system can be improved.
Data freshness. The data that feeds the analysis is not allowed to drift quietly. Each source has a refresh cadence, summarized below.
Data Freshness
O*NET task data
Refreshed annually.
BLS salary data (OEWS)
Updated annually when new OEWS data is published.
Scenario weights
Reviewed monthly.
AI task classifications
Reviewed quarterly.
What Orune Does Not Know
Orune knows public data about occupations and the context you choose to share. It does not know:
The score and the brief are tools for thinking, not answers. You are the expert on your situation. Orune is the companion that helps you read the landscape you are standing on.
Honest about what we know. Honest about what we do not.