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Challenge

Build a Construction Market Surge Predictor with Mastra AI

Predicting material procurement bottlenecks during Qatar's 6.7% construction surge requires proactive supply chain analytics. Build a workflow agent using Mastra AI to analyze raw material demand, track cement and rebar pricing, and trigger early bulk ordering.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Deploy Mastra AI TypeScript workflows to model Qatar construction growth metrics and forecast raw material shortages.

Delivery guide

How work is evaluated

Evaluation

Evaluates material demand surge forecasting accuracy and threshold alert triggers.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

action_trigger_check

Ensure bulk order trigger activates above growth threshold

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

forecast_accuracy

Accuracy of forecasted material tonnage • target: 0.94 • range: 0.85-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Build Mastra AI multi-step workflows processing macro construction investments

  • Predict material demand spikes (steel, concrete, glass) based on active project starts

  • Automate purchase order recommendations when regional inventory falls below safety thresholds

  • Generate weekly procurement risk visual summaries for project directors

Resources and assets

Reference links and supporting material

Dataset notes

Qatar construction project starts, material import tariffs, and historical pricing logs for raw building supplies.

Challenge setup

Evaluates material demand surge forecasting accuracy and threshold alert triggers.

Challenge input

JSON project pipeline data

Mastra AI

TypeScript agent framework for workflow step management

Evaluated output

JSON demand forecast

Evaluation checks
  • Ensure bulk order trigger activates above growth threshold
  • Accuracy of forecasted material tonnage • target: 0.94 • range: 0.85-1
Configured evaluation evidence
  • Benchmark: SupplyChainForecastBench
  • Forecast Accuracy target: 0.94
  • 1 public reference case
Configured runtime
  • 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

Action Space
  • Mastra AI · Required
  • Adept · Optional
  • Voiceflow · Optional
Orchestration
  • Mastra AI · Required

Evaluation contract

  • action_trigger_check · Weight 1
  • forecast_accuracy · Weight 1

Metrics

  • forecast_accuracy · Target 0.94

Benchmarks

  • SupplyChainForecastBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON project pipeline data

    Output: JSON demand forecast

    Execution harness: JavaScript

Evaluation tools

  • Mastra AI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

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