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Challenge

Build Luxury E-Commerce Verification Crew with CrewAI

Luxury fashion platforms operating on ONDC require automated verification to prevent counterfeit listings and price discrepancies. Build a multi-agent crew using CrewAI with specialized inspector roles to analyze product listings and flag compliance errors with over 95% precision.

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Challenge brief

What you are building

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

Orchestrate collaborative role-based CrewAI agents for luxury apparel authentication and compliance on open digital networks.

Delivery guide

How work is evaluated

Evaluation

Evaluates multi-agent catalog audit accuracy against gold luxury brand verification datasets.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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

flagged_reasons_non_empty

Ensures flagged listings contain specific cause tags

Binary check

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

Dimension 2

audit_precision

Precision score of counterfeit and listing fraud identification • target: 0.95 • range: 0-1

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

  • Orchestrate collaborative agents with specific roles using CrewAI

  • Build brand verification and catalog compliance evaluation tools

  • Flag pricing discrepancies and unauthorized luxury resellers on digital catalog feeds

Resources and assets

Reference links and supporting material

Dataset notes

Curated dataset of e-commerce luxury fashion listings and reseller price points.

How this agent runs

Evaluates multi-agent catalog audit accuracy against gold luxury brand verification datasets.

Challenge input

JSON containing product_title, seller_name, price_inr, MSRP_inr, image_url

CrewAI

Allows creation of autonomous multi-agent inspection teams

Evaluated output

JSON containing authenticity_status, risk_score, and flagged_reasons

Checks for
  • Ensures flagged listings contain specific cause tags
  • Precision score of counterfeit and listing fraud identification • target: 0.95 • range: 0-1
Proof of success
  • Benchmark: Luxury-Audit-Bench
  • Audit Precision target: 0.95
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • CrewAI · Required
  • crewAI · Optional
  • Zed · Optional

Evaluation contract

  • flagged_reasons_non_empty · Weight 1
  • audit_precision · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Build Luxury E-Commerce Verification Crew with CrewAI