WorkerStory OS

Multi-Category Matching Rules

A housemaid isn't matched the same way as a nurse. Each worker category activates a different rule set with category-specific weights — so the match score always reflects what actually matters for that role.

How Matching Works — 5 Steps

1

Profile Ingestion

Worker profile + employer job order loaded into matching engine

2

Category Detection

AI classifies the worker into one of 4 categories based on position

3

Rule Set Activation

Category-specific matching rules & weights activated for this match

4

Dimension Scoring

7 dimensions scored with category-adjusted weights

5

Match Score Output

Overall % match + fit reasons + risk flags returned

Live Match Simulator

Pick a category to see its rule set, then select a real worker to see how the category-specific weights produce a match score.

Domestic

Family-fit first, skills second

30%Family Integration Fit
60

Household size, children count, living arrangement compatibility

20%Reliability Score
60

Attendance history, contract completion rate, absconding risk

20%Hygiene & Health
60

Medical certificate, grooming standards, health compliance

15%Language Match
65

English/Arabic level vs employer household language needs

15%Salary Alignment
60

Expected salary within employer budget range (±15%)

Select a Domestic worker

Match Score for Zilpa

61%

Moderate Match

View Story

Weight Distribution Comparison

Rule

Domestic

Skilled

Unskilled

Professional

Reliability

20%

—

—

—

Skill

—

30%

—

—

Experience

—

25%

—

—

Language

15%

20%

—

—

Salary

15%

10%

10%

10%

Safety

—

—

10%

—

Cultural

—

—

—

15%

Different Workers. Different Rules. Better Matches.

Stop using one matching formula for every worker. Multi-Category Matching Rules give each worker type the scoring it deserves.