Work · Trust
Work Sources
Kefiw Work uses public labor-market data, occupational task information, AI exposure research, robotics signals, and human editorial review to help users compare career decisions.
Sources support the model, but users should always verify local wages, local job postings, state licensing rules, school costs, and employer requirements before paying for training.
Source categories
Labor-market data
Used for median pay, projected growth, typical education, job duties, annual openings, work environment, and related occupations.
Occupational task data
Used for task structure, work activities, skills, knowledge areas, work context, and physical or digital requirements.
AI exposure research
Used for task-level AI capability, observed AI usage patterns, theoretical versus practical exposure, and the difference between task automation and job replacement.
Robotics and automation signals
Used for service robot adoption, warehouse automation, delivery automation, autonomous vehicle exposure, and inspection, cleaning, security, and logistics automation.
Program and training assumptions
Used for tuition and fee logic, time-to-train estimates, credential payback logic, opportunity cost, completion risk, and first-year wage caution.
Human review
Used for realistic career interpretation, what people overlook, training red flags, job fit warnings, and non-generic advice.
Source freshness
Career data can change. Kefiw Work pages should be reviewed on a regular schedule.
- Occupation wage and growth pages: annually
- AI exposure pages: every 6 months
- Robotics exposure pages: every 6 months
- Training and credential guides: every 6 months
- Calculator assumptions: every 6 months
- High-traffic pages: quarterly
Work trust and methodology
Kefiw Work pages are decision support, not guarantees. Use the methodology, sources, score explanation, glossary, and advertising disclosure to understand how the advice is built.