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ButterflAI – turn 1 product photo into optimized ecommerce listing

Hacker News

ButterflAI – turn 1 product photo into optimized ecommerce listing

Hi HN, I’m Enric. I built ButterflAI to help e-commerce teams keep product pages fresh without doing constant shoots or manual copy updates. What it does? - Upload 1 product photo (or connect a Shopify store). - Generates a small set of product visuals (clean + lifestyle-style) and a short product video. - Updates product listing content (title/description/metafields) to improve SEO and conversion. Why I built it? I kept seeing teams stuck between "expensive shoots" and "stale pages". I wanted a workflow that’s fast, consistent with brand guidelines, and usable for real stores (not mockups). Why its different? Because it’s not a general-purpose image generator. It’s built specifically for e-commerce product assets. It produces the exact formats stores need (clean PDP shots, detail/angle views, before/after sets, and product infographics), with consistency across a whole catalog. I’d love feedback on: 1. Are the generated visuals actually usable for PDPs/ads? 2. What would make you trust the output (brand controls, review flow, provenance)? 3. What’s the minimum "control surface" you need (style presets, negative prompts, rules)? If you prefer the Shopify app listing: https://apps.shopify.com/butterflai Thanks!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, visual · Missing: mac, agents, macos
57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps, video · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: shopify · Missing: arr, mrr, revenue
29%29% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
13%13% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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