HOW IT WORKS
From Raw Data to Validated Ideas
We turn millions of social media conversations into actionable demand signals. Here's exactly how.
1. We Scrape Social Media
Our crawlers scan Reddit, TikTok, and X for conversations about apps, tools, and pain points. We process thousands of posts and comments daily.
- Reddit communities (r/apps, r/productivity, r/SaaS)
- TikTok comments on app review videos
- X discussions from builders and users
2. AI Identifies Demand Signals
Gemini AI analyzes each comment to detect genuine demand signals — people actively seeking solutions, expressing frustration, or requesting features.
- Pattern detection: seeking_app, frustration, alternative, feature_request
- Confidence scoring (0-1)
- Category classification
3. Quality Filtering
We filter for high-quality signals with real engagement. Low-effort comments and spam are removed automatically.
- Minimum engagement thresholds
- Spam and bot detection
- Recency weighting
4. Theme Clustering
Similar signals are grouped into themes using UMAP + HDBSCAN clustering. This reveals patterns across thousands of individual data points.
- 80+ signals on Android app demand
- 44+ signals on anti-Notion sentiment
- 75+ signals on subscription fatigue
5. Opportunity Scoring
Each theme is scored based on volume, engagement, recency, and feasibility. The best opportunities rise to the top.
- Total mentions across platforms
- Aggregate engagement (likes, replies)
- Trend direction (growing vs declining)
6. Actionable Insights
You get curated signals with the exact quotes, users, and communities. Everything you need to validate an idea or find your next project.
- Direct quotes with attribution
- User profiles to interview
- Community sizes to target
Example Signal
“Notion is useless when it comes to academics, you'll be distracted with trying to make it look pretty”
Academic productivity tool that prioritizes focus over aesthetics. Target: students frustrated with complex note-taking apps.