Instead of manual editing or one-off AI tools, MediaReduce provides workflow-based image automation that delivers consistent, professional, and marketplace-ready results at scale.
What MediaReduce does
- Automates product image compliance (dimensions, formats, backgrounds, file size)
- Provides prebuilt workflow templates for Amazon sellers, Shopify merchants, and e-commerce teams
- Enables bulk processing with predictable, repeatable output
- Powers a Shopify App that processes and replaces images directly inside Shopify Admin
- Optimizes images for speed, zoom, and conversion without manual intervention
Why it’s different
Most image tools optimize for “AI magic.” MediaReduce optimizes for consistency, compliance, and scale.
- Deterministic processing (no random results)
- Professional edge refinement and enhancement
- Platform-aware outputs (Amazon, Shopify, Etsy rules)
- Clear free → paid upgrade path for premium workflows
- Built for real production catalogs, not one-off edits
Who it’s for
- Shopify merchants managing growing product catalogs
- Amazon and Etsy sellers tired of image rejections
- Agencies handling multi-store or multi-platform listings
- E-commerce teams that need reliable automation, not manual editing
Core use cases
- Amazon main image compliance
- Shopify speed & zoom optimization
- Product image enhancement for low-quality photos
- Multi-platform export workflows
- Bulk catalog image processing
MediaReduce turns product image preparation into a reliable, automated system — so sellers can focus on selling, not fixing images.
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Feedback welcome
Current features include:
- Live material price list updated monthly (based on prices at local shops)
- Conceptual 2D/3D floor plan generation following Ghana Building Standards (development in several phases using procedural floor plan generation)
- Construction management dashboard to track project stages and conversations between project manager, mason, carpenter, etc.
- Printable material cost breakdown
TODO: A contact listing for local construction services
I would love to have feedback, thanks.
The difference here is the function's inputs are code instead of numbers, which makes LLMs useful because LLMs are good at altering code. So the LLM will try different candidate solutions, then Google's system will keep working on the good ones and throw away the bad ones (colloquially, "branch is cut").