Casino & Mixed-Use Non-Gaming Revenue Optimizer with Mastra AI
Shift in customer demographics toward digital betting forces physical regional casinos to optimize non-gaming spatial revenue (dining, entertainment, retail). Build an agentic pipeline using Mastra AI in TypeScript to reallocate square footage dynamically based on foot-traffic telemetry. Increase projected non-gaming yield by 15%+.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a spatial yield optimizer using Mastra AI agent steps to maximize non-gaming revenue for hospitality real estate.
How work is evaluated
Evaluates floorplan space reallocation recommendations and projected RevPASF improvements.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Total Area Preservation
Reallocated space must equal total available area
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Yield Optimization Factor
Projected revenue gain percentage achieved by recommendation • target: 15 • range: 5-30
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Implement Mastra AI workflows for spatial revenue optimization
Process sensor foot-traffic densities across physical floor plan zones
Dynamically reallocate underperforming gaming floor space to high-margin amenities
Output optimal floorplan square footage allocations with expected ROI
Reference links and supporting material
Foot-traffic sensor telemetry and hourly revenue per square foot records from regional casino complexes.
Challenge setup
Evaluates floorplan space reallocation recommendations and projected RevPASF improvements.
Challenge input
JSON with total_sqft, current_slot_sqft, dining_foot_traffic_index, slot_rev_per_sqft, dining_rev_per_sqft
Mastra AI
Provides modular TypeScript workflows and step engine
Evaluated output
JSON with recommended_slot_sqft, recommended_dining_sqft, projected_rev_increase_pct
- Reallocated space must equal total available area
- Projected revenue gain percentage achieved by recommendation • target: 15 • range: 5-30
- Benchmark: SpatialYieldBench
- Yield Optimization Factor target: 15
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Mastra AI · Required
- Bland AI · Optional
- Vercel AI Gateway · Optional
- Mastra AI · Required
- Vercel AI Gateway · Optional
Evaluation contract
- Total Area Preservation · Weight 1
- Yield Optimization Factor · Weight 1
Metrics
- Yield Optimization Factor · Target 15
Benchmarks
- SpatialYieldBench
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with total_sqft, current_slot_sqft, dining_foot_traffic_index, slot_rev_per_sqft, dining_rev_per_sqft
Output: JSON with recommended_slot_sqft, recommended_dining_sqft, projected_rev_increase_pct
Execution harness: JavaScript
Evaluation tools
- Mastra AI · Agent System Framework
Recipe freeze marker
No recipe freeze time is recorded.