AI-powered trend prediction system. Identifies opportunities 2-10 years before mainstream adoption with 70% accuracy.
Trends are visible everywhere, but by the time they're mainstream, the opportunity is gone. Most trend analysis is reactive - showing what's already popular rather than predicting what will become popular.
While building Teneo, I needed to understand content strategy and market timing. What topics would be hot in 6 months when the books were ready?
AI-powered system that monitors weak signals across multiple platforms, identifies emerging patterns, and predicts mainstream adoption 173 days in advance with 70% accuracy.
Reddit, YouTube, Amazon, GitHub, TikTok, HackerNews. 10M+ data points analyzed daily.
70% prediction accuracy with 173-day average lead time before mainstream adoption.
Next.js 14 with App Router, Tailwind CSS v4, shadcn/ui, Vercel deployment
FastAPI with async support, SQLite + Redis, Railway deployment with auto-scaling
GPT-4 contextual analysis, custom scoring algorithms, real-time WebSocket processing
BookRadar serves as the strategic intelligence layer, providing trend insights that optimize timing and content across the entire AI Publishing Stack.
Suggests trending topics for book creation 6 months ahead of mainstream adoption
Informs design trends and visual styles that will resonate with future audiences
With 173-day lead time and 70% accuracy, BookRadar enables authors to position their books perfectly for emerging trends rather than chasing topics that are already saturated.
Content strategy and market timing insights
Design trend analysis for cover generation
Improving dashboard UX and trend visualization
Pushing accuracy above 75% and reducing false positives