Hyperfold AI

Catalog Optimize

Transform marketing copy into agent-readable product data.

Overview

The hyperfold catalog optimize command rewrites product descriptions for AI agent comprehension. Marketing-heavy copy full of superlatives and promotional language is converted to fact-dense, structured content that agents can parse efficiently.

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# Optimize entire catalog for agent readability
$ hyperfold catalog optimize
 
> [Scan] Found 1,489 products
> [Job] Starting batch optimization job: job_opt_445
> [Progress] Optimizing descriptions...
> [Progress] Analyzing images...
> [Progress] Generating embeddings...
 
✓ Optimization complete
 
SUMMARY
  Products processed: 1,489
  Descriptions improved: 892
  Images analyzed: 2,847
  Embeddings updated: 1,489
 
READABILITY SCORE
  Before: 0.62
  After: 0.89 (+43%)
Optimization creates a parallel "agent-readable" version of your descriptions. Original marketing copy remains unchanged for human-facing channels.

Agent Optimization

Different AI models process text differently. Optimize for specific buyer agents:

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# Optimize for a specific buyer agent model
$ hyperfold catalog optimize --target="gpt-4o"
 
> [Config] Target model: gpt-4o
> [Style] Optimizing for: concise, fact-dense, structured
 
✓ Optimized for GPT-4o comprehension
 
# Available targets
$ hyperfold catalog optimize --list-targets
 
TARGET          STYLE
gpt-4o          Concise, structured, fact-dense
gpt-4o-mini     Ultra-concise, key facts only
claude-3        Detailed, contextual, nuanced
gemini-pro      Structured, attribute-focused
generic         Balanced for all agents (default)

Optimization Styles

TargetDescription
gpt-4oConcise bullet points, explicit specifications, structured format
gpt-4o-miniUltra-compact, key facts only, minimal prose
claude-3Detailed context, nuanced descriptions, use-case focused
gemini-proStructured attributes, comparative context, feature lists
genericBalanced approach suitable for any agent (default)

Description Rewriting

See how optimization transforms product descriptions:

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# Example transformation during optimization
 
BEFORE (Marketing-focused):
"Our AMAZING AeroRun X2 will TRANSFORM your running experience!
 These incredible shoes feature cutting-edge technology that
 professional athletes LOVE! ⭐⭐⭐⭐⭐ Don't miss out - BUY NOW
 and get FREE shipping! Limited time offer!"
 
AFTER (Agent-optimized):
"AeroRun X2 Marathon Running Shoe
 
 Specifications:
 - Weight: 280g
 - Heel drop: 8mm
 - Upper: Gore-Tex waterproof mesh
 - Sole: EVA foam with carbon plate
 - Waterproof rating: IPX4
 
 Best for: Marathon, trail running, wet conditions
 Target user: Competitive runners, athletes
 
 Care: Machine washable, air dry"

Selective Optimization

Optimize specific products or filter by criteria:

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# Optimize specific products
$ hyperfold catalog optimize --product="prod_aero_x2"
 
# Optimize products matching criteria
$ hyperfold catalog optimize --filter="category:footwear"
$ hyperfold catalog optimize --filter="updated_after:2025-12-01"
 
# Optimize products with low readability scores
$ hyperfold catalog optimize --filter="readability:<0.7"
 
# Preview changes without applying
$ hyperfold catalog optimize --dry-run
 
> [Preview] prod_001: Description rewrite (score: 0.45 → 0.91)
> [Preview] prod_002: Image tags added (5 new tags)
> [Preview] prod_003: No changes needed (score: 0.88)
Products with readability scores above 0.85 are skipped by default. Use --force to re-optimize all products.

Image Analysis

Extract visual attributes from product images using multimodal AI:

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# Analyze product images for visual tags
$ hyperfold catalog optimize --images-only
 
> [Vision] Analyzing 2,847 product images...
> [Extract] Extracting colors, styles, materials...
 
EXAMPLE OUTPUT for prod_aero_x2:
  Primary color: Blue (#2563EB)
  Secondary: White, Black
  Style tags: athletic, modern, streamlined
  Material: mesh, synthetic, rubber
  Features: reflective elements, thick sole
 
# Force re-analysis of all images
$ hyperfold catalog optimize --images-only --force
 
# Skip image analysis (faster)
$ hyperfold catalog optimize --no-images

Extracted Attributes

AttributeDescription
ColorsPrimary, secondary, and accent colors with hex codes
Style TagsAesthetic descriptors (modern, vintage, minimalist)
MaterialsVisible material types (leather, metal, fabric)
FeaturesVisual features (buttons, zippers, patterns)
VibeLifestyle/mood associations (professional, casual)

Embedding Refresh

Update vector embeddings for improved semantic search:

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# Refresh vector embeddings only
$ hyperfold catalog optimize --embeddings-only
 
> [Embed] Regenerating embeddings for 1,489 products...
> [Model] Using: text-embedding-3-large
> [Index] Updating Vertex AI Vector Search...
 
✓ Embeddings refreshed
 
# Use specific embedding model
$ hyperfold catalog optimize --embeddings-only --model="text-embedding-3-small"
 
# Check embedding health
$ hyperfold catalog embeddings status
 
EMBEDDING STATUS
  Total products: 1,489
  With embeddings: 1,489 (100%)
  Stale (>30 days): 23
  Missing: 0
 
INDEX STATUS
  Vector dimension: 1536
  Index type: HNSW
  Last rebuild: 2025-12-18T14:30:00Z
Test your optimized catalog with semantic search.