Data-Trained AI Content Engine Playbook

March 6, 2026

Tools used

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Build a niche-specific AI content engine trained on thousands of real high-performing posts to generate data-backed, non-generic content.

Jan Brochwicz

How it works

Step 1: Select Target Creators

Define the dataset before building the engine.

Pick top creators in the niche:
- Manual selection
- Or use Scripe for automated discovery
Criteria:
- Same ICP audience
- Consistent posting cadence
- High engagement relative to follower count
- Clear niche positioning

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Step 2: Scrape & Aggregate Content

Aggregate all historical content into a single dataset.

Use Apify to:
- Scrape posts
- Extract captions
- Capture engagement metrics
- Download post metadata
Pull:
- Likes
- Comments
- Reposts
- Post timestamps
- Media URLs

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Step 3: Clean & Structure Data

Prepare dataset without manual spreadsheet work.

Upload raw data into Cursor
Use AI chat inside Cursor to:
- Clean inconsistencies
- Standardize columns
- Remove duplicates
- Extract structured fields

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Step 4: Generate Structured Assets

From the cleaned dataset, produce:

Merged clean CSV
All post graphics downloaded
Posts ranked by performance:
- Engagement rate
- Hook strength

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Step 5: Run Deep Content Analysis

Analyze patterns using Claude.

Upload clean dataset into a Claude Project.
Run structured analysis to generate:
- Performance Playbook
  - What formats win
  - Best posting times
  - Hook rankings
- Content Library
  - All posts categorized
    - Tagged by:
      - Topic
      - Format
      - Hook type
- Sentence Library
  - Reusable high-performing sentences
  - Structured JSON output
  - Categorized by:
    - Hooks
    - Transitions
    - CTAs

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Step 6: Build the Custom AI Content Engine

Convert analysis into an operational engine.

Create a dedicated Claude Project trained on:

1000s real niche posts
700+ reusable high-performing sentences
Ranked hooks
Voice pattern mapping

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