Prohect Asset Bin with AI Semantic Auto Placement - A Feature Request!!
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Rabie Shawwa
The Problem: The Asset Bottleneck Gamma’s generation speed is fantastic, but inserting custom, real-world assets remains a highly manual friction point. When building a deck that requires specific images (e.g., 50 product photos, client assets, or team headshots), the AI currently generates its own images or leaves empty placeholders. Users are then forced to manually upload and map their real images to individual cards one by one. This manual drag-and-drop process breaks the speed advantage of using an AI generator.
The Solution: A Context-Aware Asset Pool Introduce a Project Asset Bin that integrates directly with the AI generation engine:
1. Bulk Upload: Allow users to upload a folder of project-specific images to a dedicated bin before or during the prompt phase.
2. AI Semantic Matching: Gamma's AI uses computer vision to analyze and index the contents of the uploaded images.
3. Auto-Placement: During layout generation, instead of defaulting to web searches or AI image generation, Gamma automatically pulls the most contextually relevant image from the user's Asset Bin and slots it into the correct layout placeholder.
The Impact While Gamma’s recent additions (like the AI Image Dashboard) help manage generated images, this feature would solve the ingestion of existing user media. It bridges the gap between digital asset management and AI layout creation, turning an hour of manual image placement into an instant, automated workflow.
Nik Payne (Gamma design)
Rabie Shawwa this is a super thoughtful writeup, thank you. Totally get how the “upload 50 real images then drag them in one by one” thing kills the whole speed advantage, I’ll pass this along to the team.
Quick couple questions so we scope it right:
1) When you bulk upload, do you usually have filenames/metadata we could lean on (SKU, person name, etc), or is it mostly just the pixels?
2) What would “correct placement” mean in your ideal world: match images to specific slides by title/section, or just fill any image placeholders with best-fit and let you review/approve in a pass?
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Rabie Shawwa
Thanks for the quick follow-up. Here is how I see those two scenarios playing out in practice:
1) Ingestion: Hybrid is necessary (Pixels + Metadata)
While enterprise users might have clean taxonomy (e.g., SKU_4920_Front.png), the reality is most users will bulk-dump files named IMG_8492.jpg or Screenshot_04.png.
Ideally, the system should ingest whatever metadata/filenames exist and weight them heavily (especially for things like team names or specific part numbers). However, it absolutely needs a Vision API fallback to auto-tag/embed the "just pixels" uploads so the system knows IMG_8492.jpg is a picture of a modern kitchen.
2) Placement: Best-fit fill with a review state
Strictly matching images to specific slides by title creates too much friction, especially if the AI dynamically alters the presentation structure during generation.
The ideal flow is semantic best-fit with a review layer. The AI should fill the placeholders based on the context of the generated slide. However, there needs to be a human-in-the-loop approval step. A great UI for this would be placing the images in the deck but flagging them (e.g., an "Auto-placed" badge), coupled with a side-panel "Asset Tray" showing what was placed and what was left over. This allows the user to quickly scan, approve, or drag-and-swap the remaining assets. ALTERNATIVELY- failing my suggestions- better to simply have a bin that I can dump as many images as I want and then manually select (inserts)- that's probably easier and quicker. At the moment Gamma is annoying. It does a brilliant job on structuring but when it comes to me applying actual real relevant images (not ai generated)- I will have to go manually into my device and search. Gamma only keeps images of the ai generated ones but not my uploaded ones!
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Rabie Shawwa
Trust me- you do this and the world will end up using it.