This MVP explores how we can scale ingredient intelligence in a transparent, human-centered way, leveraging conversational AI to simplify dense INCI data into readable, trustworthy insights.
I’m leading UX content strategy and system design, while Keon builds the GenAI backend. Together, we’re designing a pipeline that blends structured data sourcing with AI outputs to improve clarity, trust, and usability.

Key skills: UX writing, AI content systems, LLM-informed design, collaboration with engineering

Skincare can be overwhelming, and most tools add friction instead of clarity. We’re closing that gap with a science-first conversational AI that translates ingredient lists into simple, user-friendly explanations, without fear-mongering.
I’m designing the interaction model and content system, focusing on accessible language and UX clarity. Working with Keon on the GenAI implementation, we’re iterating quickly to balance technical complexity with user trust and product usability.

Key skills: AI UX design, language simplification, GenAI collaboration, agile content iteration