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Challenge

Mastra AI Native Services Ad Counter-Campaign Analyzer

Hyper-competitive local home services platforms face disparaging comparative marketing campaigns. Build a real-time ad monitoring and legal/PR triage engine using Mastra AI in TypeScript to detect disparaging claims and generate counter-playbooks.

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Challenge brief

What you are building

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

Construct an automated ad campaign monitor and risk response system using Mastra AI.

Delivery guide

How work is evaluated

Evaluation

Evaluates advertisement disparagement identification and response selection.

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

disparagement_detected

Ensures disparaging copy is correctly flagged.

Binary check

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

Dimension 2

classification_precision

Precision of ad claim classification • 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

  • Use Mastra AI workflows in TypeScript for multi-channel ad copy monitoring

  • Extract comparative product claims and score disparagement severity

  • Trigger dynamic legal alert notifications and public response playbooks

Resources and assets

Reference links and supporting material

Dataset notes

Digital ad copy corpus with ASCI regulatory tags and target brand labels.

Challenge setup

Evaluates advertisement disparagement identification and response selection.

Challenge input

JSON ad copy input

Mastra AI

Mandatory framework requirement

Evaluated output

JSON risk assessment output

Evaluation checks
  • Ensures disparaging copy is correctly flagged.
  • Precision of ad claim classification • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: Ad-Compliance-Bench
  • Classification Precision target: 0.95
  • 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
  • Aim · Optional
  • Deepgram · Optional
Orchestration
  • Mastra AI · Required

Evaluation contract

  • disparagement_detected · Weight 1
  • classification_precision · Weight 1

Metrics

  • classification_precision · Target 0.95

Benchmarks

  • Ad-Compliance-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON ad copy input

    Output: JSON risk assessment output

    Execution harness: JavaScript

Evaluation tools

  • Mastra AI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Mastra AI Native Services Ad Counter-Campaign Analyzer