The AI Brain Behind Ingredient Transparency
TL;DR:
We’re continuing to build an AI-powered ingredient interpreter for skincare, designed to help users decode formulations with scientific precision and user-first language. This update reveals the latest design evolutions, schema decisions, and GenAI behavior prototypes.
This post builds on the original project overview. You can read the full context here. → Original case study
Defining the Output Format
To make the system usable for both consumers and professionals, we defined a structured schema for ingredient insights that balances technical depth with readability.
MVP Architecture: Keon’s Local Prototype
Keon spun up a local dev stack using Ollama to prototype and benchmark LLM output across different prompt styles. We’re using:
Frontend: Lightweight React app for rapid input + result loop.
API Layer: Gin-powered REST API connecting to Ollama.
LLM Runtime: Ollama serving locally fine-tuned models for privacy and iteration speed.
Example Output
React Frontend → Gin REST API → Ollama LLM Engine
Why It Works: Fast iterations, low-latency prototyping, and versionable LLM behavior that adapts to both consumer questions and professional use cases.
Sourcing Data
While Keon stress-tests prompt consistency, I’m curating a vetted data pipeline, focusing on medically reviewed, user-readable sources. Our ingestion process prioritizes:
Evidence-based summaries
INCI-standard nomenclature
Contextual safety notes (e.g., photo-sensitivity, pregnancy concerns)
The long-term vision: ingredient insights with traceability, explainability, and trust flags baked in.
Here’s a screenshot of the early frontend where it all comes together.
Phases:
Phase 1: Testing + Iteration (Current Phase)
Integrate with LLM or retrieval-augmented model
Conduct usability testing on language clarity and risk phrasing
Align outputs with accessibility and enterprise design systems
Phase 2: Testing + Iteration
Begin user testing with real ingredient lists
Test upload and paste workflows
Build a trust-feedback loop to guide future improvements
Future Plans
Layer in user profiles (e.g., acne-prone, sensitive skin, rosacea)
Enable “smart” questions (e.g., “Is this pregnancy safe?”)
Develop a Chrome extension for ingredient popovers while browsing.