Build a Mastra AI Quick Commerce & D2C Merchant Risk Pipeline
Rapid onboarding of D2C sellers on quick commerce platforms increases exposure to operational vendor risk. Build a TypeScript workflow using Mastra AI to evaluate seller inventory health, fulfillment reliability, and return rates, producing automated merchant risk scores.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a Mastra AI TypeScript workflow with built-in state management and memory to compute merchant risk levels for quick commerce networks.
How work is evaluated
Evaluates workflow completion and accuracy of merchant risk categorization.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
workflow_status_check
Verifies Mastra workflow completed execution
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
risk_tier_accuracy
Match score against risk model output • target: 1 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Construct Mastra AI Agent workflows using TypeScript
Leverage Mastra memory and store drivers for historical seller metrics
Compute composite merchant risk scores based on operational metrics
Trigger automated credit limit adjustments based on seller risk profiles
Reference links and supporting material
Synthetic merchant fulfillment operational dataset containing return rates and quarterly GMV values.
How this agent runs
Evaluates workflow completion and accuracy of merchant risk categorization.
Challenge input
JSON payload of seller metrics
Mastra AI
TypeScript framework for workflow state management
Evaluated output
JSON object with risk_tier and max_credit_limit_inr
- Verifies Mastra workflow completed execution
- Match score against risk model output • target: 1 • range: 0-1
- Benchmark: E-Commerce Merchant Risk Benchmark
- Risk Tier Accuracy target: 1
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Mastra AI · Required
- BoTorch · Optional
- Fixie · Optional
- Mastra AI · Required
Evaluation contract
- workflow_status_check · Weight 1
- risk_tier_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.