An r/dropshipping post asked which AI tools find winning products. Cross-platform discovery beats Amazon-only tools when winners spread across Walmart and TikTok Shop. This tutorial builds the Scavio-backed scorer.
Prerequisites
- Python 3.10+
- Scavio API key
- An LLM API key
Walkthrough
Step 1: Define seed categories
Niches you sell into.
Python
CATEGORIES = ['home fitness equipment under $50', 'eco-friendly kitchen gadgets', 'phone accessories trending']Step 2: Pull Amazon + Walmart per category
Cross-marketplace discovery.
Python
import os, requests
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
def cross(c):
a = requests.post('https://api.scavio.dev/api/v1/amazon/search', headers=H, json={'query': c}).json()
w = requests.post('https://api.scavio.dev/api/v1/walmart/search', headers=H, json={'query': c}).json()
return a, wStep 3: Reddit demand signal
r/dropshipping, r/Flipping, niche subs.
Python
def demand(c):
return requests.post('https://api.scavio.dev/api/v1/reddit/search', headers=H, json={'query': f'{c} winning product 2026'}).json()Step 4: LLM scoring
Pass each candidate to an LLM with rubric.
Text
# Rubric: margin, demand, competition, ship-speed, returns risk.
# Prompt: 'Score 0-100, justify in 1 sentence.'Step 5: Rank and surface top 10
Daily 9 AM email with top candidates.
Text
# Cron daily; output to email or Sheets.Python Example
Python
# 3 categories × 3 calls each = 9 credits/day = ~$0.04/day = $1.20/mo Scavio.
# LLM scoring on Groq Llama-3 at ~$0.10/day.JavaScript Example
JavaScript
// Same in TS.Expected Output
JSON
Daily ranked list of 10 cross-platform product candidates with margin, demand, and competition scores. Beats Amazon-only tools by surfacing Walmart and Reddit-flagged winners.