AI assessment
Operations
Retail Match Maker
Automated talent–client matching to enable scalable growth.
60% of matches now happen automatically, doubling the number of shifts while freeing the team to focus on sales expansion.
Industry
HR
Role
Dweet
Team
4 Engineers, 1 Product Manager
Too many variables, not enough structure
Understanding how clients think about talent
1. Clients valued reliability and continuity (“send me the same person as last week”) but lacked tools to do so.
2. Feedback loops were inconsistent, limiting our ability to learn from previous shifts.
3. Heavy dependence on manual placements created bottlenecks and variability in match quality.
Designing a self-learning matching system
1. Client feedback loops
Integrated post-shift ratings directly into timesheets to improve completion rates. Automatically promoted top performers into Favourites and flagged issues via a Strikes system. Introduced blocking to prevent repeat bookings with unsuitable talents.
2. Favourite-driven automation
Built a “My Favourites” page where clients can curate, view, and manage preferred talents. Added logic for My Team Favourites, Brand Favourites, and Dweet Recommended suggestions. Simplified lists (max 12) and adjusted sorting rules based on recent shifts and performance.
3. Automated booking logic
The system now auto-selects favourites before suggesting new matches.
Scaling human intuition through data
50% of shifts are booked automaticly
This allowed to double the number of shifts while freeing the team to focus on sales expansion.
2x in Favourites adoption
Clients now recognise and curate talent pools.
Better matching = better service quality
Improved match quality and reduced cancellations, leading to faster, more reliable staffing.