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Challenge

Build a Google ADK Brand Protection Agent with Lovable Web Prototyping

Rapidly growing D2C brands face trademark disputes and unauthorized product listings on e-commerce platforms. Create a Google ADK agent using Gemini multimodal models to identify counterfeit brand assets and output a functional Lovable UI dashboard to resolve trademark conflicts across retail channels.

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Challenge brief

What you are building

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

Develop a Google ADK visual inspection agent that flags illegal trademark usage and generates structured conflict reports integrated with Lovable prototypes.

Delivery guide

How work is evaluated

Evaluation

Evaluates visual and text trademark collision identification precision.

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

detection_match

Confirms infringement detection trigger

Binary check

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

Dimension 2

confidence_score

Model confidence on trademark collision • target: 0.94 • 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

  • Deploy Google ADK multi-modal agents with Gemini models for visual brand match analysis

  • Implement fuzzy string and trademark vector search logic for brand name detection

  • Integrate with Lovable tool interfaces to expose UI dashboards for trademark enforcement

  • Generate automated legal cease-and-desist draft reports

Resources and assets

Reference links and supporting material

Dataset notes

E-commerce product catalog listings with synthetic brand copy and trademark registry entries.

Challenge setup

Evaluates visual and text trademark collision identification precision.

Challenge input

JSON listing object with brand text and image reference

Google ADK

Multimodal agent development using Gemini

Lovable

Generates dashboard interface prototypes

Evaluated output

JSON triage outcome with collision confidence and summary

Evaluation checks
  • Confirms infringement detection trigger
  • Model confidence on trademark collision • target: 0.94 • range: 0-1
Configured evaluation evidence
  • Benchmark: IP Dispute Detection Benchmark
  • Confidence Score target: 0.94
  • 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
  • Google ADK · Required
  • Lovable · Optional
  • Google · Optional

Evaluation contract

  • detection_match · Weight 1
  • confidence_score · Weight 1

Metrics

  • confidence_score · Target 0.94

Benchmarks

  • IP Dispute Detection Benchmark
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON listing object with brand text and image reference

    Output: JSON triage outcome with collision confidence and summary

    Execution harness: Python

Evaluation tools

  • Google ADK · Agent Framework
  • Lovable · UI Engineering Tool

Recipe freeze marker

No recipe freeze time is recorded.

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