Skip to main content
Back to challenges
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.

Business OperationsHosted by Vera
Sign in free to start
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

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

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.

Challenge setup

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

Evaluation checks
  • Ensures flagged listings contain specific cause tags
  • Precision score of counterfeit and listing fraud identification • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: Luxury-Audit-Bench
  • Audit Precision target: 0.95
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python 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
  • CrewAI · Required
  • crewAI · Optional
  • Zed · Optional

Evaluation contract

  • flagged_reasons_non_empty · Weight 1
  • audit_precision · Weight 1

Metrics

  • audit_precision · Target 0.95

Benchmarks

  • Luxury-Audit-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing product_title, seller_name, price_inr, MSRP_inr, image_url

    Output: JSON containing authenticity_status, risk_score, and flagged_reasons

    Execution harness: Python

Evaluation tools

  • CrewAI · Agent Orchestration

Recipe freeze marker

No recipe freeze time is recorded.

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