ShopPulse AI
The User: Small business owners, online shop owners and social media sellers who want to advertise their products but have no marketing team, design skills, or big ad budget. The Problem: Small businesses struggle to create ads that match what is trending on social media. Following trends on TikTok, Instagram Reels, X and Reddit by hand takes a lot of time, and hiring a designer or video editor is expensive. As a result, many owners publish weak ads or none at all. ShopPulse AI solves this by analyzing trending ads and generating an ad image or video for the owner's chosen product.
Preliminary score
- Value for the user16.0 / 25
A specific problem and a clear outcome from solving it.
- Prototype and use of AI14.0 / 30
A working core scenario, and what AI actually contributes to the result.
- Quality testing8.5 / 20
Tests, examples of failures, and comparison with the current approach.
- Feasibility7.0 / 15
Data requirements, running costs and a clear next step.
- Originality4.5 / 10
A meaningful difference from common solutions.
What the judges wrote
- Claude Opus 5.5Preliminary47/100
- Strengths
- Your target user is clear and real: small sellers with no marketing team who need ads that match current social media trends. Your flow goes beyond generating an ad from a prompt, because it first pulls trending content through Apify and analyzes it with Gemini and Claude. You were honest about your testing: you ran four concrete products (blender, earbuds, serum, smartwatch) through the whole flow and openly said that image and especially video generation fail intermittently. A live deployed demo and a video show you tried to make the prototype inspectable.
- To improve
- Pick a measurable outcome and report it, for example time from product to finished ad, cost per ad compared with a freelance designer, or the engagement or CTR of a generated ad compared with an owner's existing ad. Make your testing systematic. Log the success and failure rate of image and video generation across runs, record why each failure happened (timeouts, rate limits, content filters), and show examples of trend-analysis output that was right and wrong. Add per-ad running costs for the scraping and generation APIs, and a concrete next step such as a pilot with a few named sellers. Explain how the trend analysis changes the generated creative, so it is clearly more than a generic ad generator.
- GPT-6 AstraPreliminary53/100
- Strengths
- Your focus on small sellers without marketing teams connects trend research and ad production to a clear business pain. You tested four distinct product types and candidly reported intermittent generation failures, identifying video as the weakest component. Your deployment and local setup instructions provide a concrete path for trying the prototype.
- To improve
- Your next iteration should document a successful end-to-end run showing which collected trends informed the ad and what Gemini and Claude each contributed. Repeat your four-product tests and record success rates, failure causes, generation times, and output usability against a manual-workflow baseline. Define an adoption metric such as time and cost per usable ad, including retries, and document social-data access requirements and API costs. Prioritize reliable image generation before expanding the video workflow.
The team
Məcidov Rəşid
data
Aslanlı AslanLead
data
Ziyadlı Riyad
product