Your ad creation cheat code

Turn Meta Ads into
LLM-Ready Data

Extract, transcribe, and structure competitor ads into clean formats that your AI tools can analyze. Stop manual data cleaning—get insights from thousands of ads instantly.

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Export as CSV/JSON for AI analysis
Video transcripts included

One click away from 1000+ ads

BentoBear turns competitor ads into insights with one click. Automatically turns ads into data that is perfect for AI.

1

Paste Meta Ad Library URL

Browse Meta's Ad Library to find competitor ads, then paste the URL into BentoBear

f
🔍
Nike
1,247 results
N
NikeSponsored

2 days ago

🏃‍♀️ Ready to crush your next run? Our new Air Zoom series combines comfort with performance. Experience the difference that innovative cushioning can make.

0:30
Active since Nov 18, 2024
View on Facebook
2

Get LLM-Ready Analysis

Get structured data with video transcripts and captions ready for AI analysis

nike_ad_analysis.md
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Export
# Nike Ad Analysis

## Ad Metadata
- **Page Name**: Nike
- **Ad Title**: New Air Zoom Series Launch
- **CTA**: Shop Now
- **Status**: Active
- **Start Date**: November 18, 2024
- **Ad Type**: Video Advertisement

## Ad Copy
🏃‍♀️ Ready to crush your next run? Our new Air Zoom series combines comfort with performance. Experience the difference that innovative cushioning can make.

## Video Transcript
*[00:00 - 00:05]*
Runner laces up Nike Air Zoom shoes in slow motion

*[00:05 - 00:15]*
**Narrator (Energetic, motivational)**: "Every step forward starts with the right foundation. The new Nike Air Zoom series doesn't just support your run—it amplifies it."

*[00:15 - 00:25]*
Multiple runners in different environments - city streets, trails, track

*[00:25 - 00:30]*
**Narrator**: "Nike Air Zoom. Feel the difference. Make the difference."

## Marketing Insights
- **Target Audience**: Active lifestyle, fitness enthusiasts
- **Key Message**: Performance enhancement through technology
- **Visual Strategy**: Dynamic movement, diverse environments
- **Emotional Appeal**: Empowerment and achievement

## Structured Data
```json
{
  "ad_id": "nike_air_zoom_launch_2024",
  "brand": "Nike",
  "product": "Air Zoom Series",
  "sentiment": "positive",
  "keywords": ["running", "performance", "comfort", "innovation"],
  "video_duration": 30,
  "transcript_word_count": 42
}
```
Lines: 44Characters: 1380
AI ProcessedReady for LLM
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Turn competitor ads into insights

Clean, structured data formats that work seamlessly with ChatGPT, Claude, and your custom LLMs

Bulk Data Extraction
Extract thousands of ads from Meta Ad Library URLs. Get clean metadata, targeting info, and creative assets in structured formats ready for LLM analysis
Video-to-Text Conversion
Automatic speech-to-text transcription for video ads. Transform audio content into searchable, analyzable text data that LLMs can process
LLM-Ready Exports
Download as CSV or JSON with clean, consistent formatting. Perfect for feeding into ChatGPT, Claude, or custom AI models for analysis
Structured Search
Search across ad copy, transcripts, and metadata with structured data outputs. Find patterns and export results in formats LLMs understand
Data Analytics Ready
Track collection history with consistent data schemas. Build datasets over time that maintain structure for longitudinal AI analysis
Cost-Effective Scaling
Pay per ad extracted, not per API call. Build large datasets for AI training and analysis without subscription overhead

How it works

Three steps to turn messy ad data into structured LLM inputs

1

Input URL

Paste any Meta Ad Library URL to identify the ads you want to extract and transform

2

Extract & Process

AI extracts all metadata, transcribes video content, and structures everything into clean data formats

3

Export for AI

Download clean CSV/JSON data ready to feed into ChatGPT, Claude, or your custom LLM workflows

Example Meta Ad Library URL:
https://www.facebook.com/ads/library/?active_status=all&ad_type=all&country=US&q=Nike

Simple Pricing

Pay per ad processed. Skip the data cleaning headaches and get straight to AI analysis.

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