In this episode of the AI Chronicles podcast, host Kyle James interviews Courtney Kos, founder of Prévoir, an AI-driven planning tool for the fashion industry. They discuss how Prévoir leverages computer vision and machine learning to help fashion brands analyze product performance, optimize assortments, and forecast trends. Courtney shares insights on the challenges faced by fashion brands in data analysis, the onboarding process for Prévoir, and the future of AI in fashion analytics.
Links:
Prévoir.ai: prevoir.ai
GPT Trainer: Automate anything with AI -> gpt-trainer.com
Key Moments:
- Prévoir was founded to address the lack of fashion-specific data analysis tools.
- Many fashion brands still rely on Excel spreadsheets for inventory planning.
- Computer vision has significantly improved, making data analysis easier and cheaper.
- User interface design is crucial for effective data presentation.
- Brands can onboard Prévoir in about 30 minutes, streamlining the integration process.
- Contextual data is essential for understanding sales performance.
- Fashion brands are looking to keep teams leaner amidst industry challenges.
- Prévoir is currently in beta, gathering feedback from pilot brands.
- AI can automate reporting processes for fashion brands.
- Internal data is the primary driver for fashion planning, not just external trends.
Chapters
- (00:00:00) - Introduction to AI in Fashion
- (00:01:49) - The Birth of Prévoir
- (00:02:00) - Identifying the Need for AI
- (00:04:52) - Implementing Computer Vision in Fashion
- (00:08:10) - Onboarding Process for Brands
- (00:10:40) - Results and Feedback from Clients
- (00:12:23) - Future AI Initiatives at Prévoir
- (00:15:58) - Conclusion and Resources
