Qasil

AI-powered cosmetic ingredient analyzer. Get personalized safety reports based on your skin type and allergies with a well-guided skincare routine

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Inspiration

The inspiration for Qasil came from a personal frustration: spending 20+ minutes researching cosmetic ingredients only to feel more confused than informed. With products containing 20-40 ingredients with complex chemical names, and 88% of consumers unable to understand safety implications, we realized there was a critical gap in accessible, personalized skincare safety information.

We asked ourselves: What if AI could analyze ingredients in seconds, adapt to individual allergies and skin types, and validate its own outputs for accuracy? This question led us to explore multi-agent AI systems not just simple pipelines, but truly autonomous agents that could collaborate, self-correct, and make intelligent decisions.

What it does

Qasil is a self-correcting multi-agent AI system that analyzes cosmetic ingredients in under 10 seconds, delivering personalized safety assessments based on user allergies and skin type.

Key Features:

  • Personalized Analysis: Cross-references ingredients with your specific allergies and skin type (sensitive, oily, dry, combination)
  • Multi-Agent Collaboration: Four specialized AI agents (Supervisor, Research, Analysis, Critic) work together to ensure accurate results
  • Self-Correction: Critic Agent validates every analysis and forces retries if quality standards aren't met
  • Routine Management: Build and analyze your complete skincare routine, detect ingredient conflicts
  • Product Comparison: Compare up to 3 products side-by-side with AI-powered recommendations
  • Adaptive Language: Adjusts technical depth based on your expertise level (beginner to expert)

The Result: From 20 minutes of manual research to 10 seconds of validated, personalized analysis.

How we built it

Qasil Architecture

Qasil's architecture demonstrates true agentic AI through autonomous decision-making and dynamic workflow orchestration:

1. Multi-Agent System (LangGraph + Gemini 2.0 Flash)

  • Supervisor Agent: Strategic router that analyzes workflow state and decides which agent to activate next
  • Research Agent: Autonomously selects tools (Qdrant vector search vs. Tavily web search) based on ingredient type and confidence scores
  • Analysis Agent: Generates personalized safety reports adapting to user profile (skin type, allergies, expertise level)
  • Critic Agent: Quality validator with authority to reject outputs and force retries (max 5 attempts)

2. Intelligent Tool Selection

  • Qdrant Cloud vector database with 400+ ingredient embeddings (384-dimensional)
  • Tavily API for web search fallback when confidence < 0.7
  • FastMCP framework for custom tools (ingredient_lookup, safety_scorer, allergen_matcher)

3. Memory & Personalization

  • Redis Cloud for session management and user profiles
  • Mem0 for contextual memory that learns preferences across sessions
  • LangGraph state machine for short-term memory during workflow execution

4. Full-Stack Implementation

  • Backend: FastAPI with JWT authentication, CORS middleware, rate limiting
  • Frontend: React + Vite with modern UI, OCR support, real-time updates
  • Observability: LangSmith tracing for complete agent decision visibility

5. Deployment

  • Frontend: Vercel (automatic deployments from main branch)
  • Backend: Hugging Face Spaces (Docker-based, 16GB RAM, 8 CPU cores)

Challenges we ran into

1. Self-Correction Without Infinite Loops

  • Challenge: How do we let the Critic Agent reject outputs without creating infinite retry loops?
  • Solution: Implemented max retry limits (5 attempts) with escalation logic. After max retries, Supervisor returns partial results with disclaimer rather than failing completely.

2. Dynamic Tool Selection

  • Challenge: Not all ingredients need the same research approach. Using both vector search and web search for every ingredient wastes time and API costs.
  • Solution: Research Agent autonomously decides tool selection based on ingredient type and confidence scores. Common ingredients → Qdrant only. Scientific names → Qdrant first, Tavily fallback if confidence < 0.7. Unknown compounds → Tavily immediately.

3. Personalization at Scale

  • Challenge: Generic safety databases don't account for individual differences (allergies, skin type, expertise level).
  • Solution: User profiles stored in Redis with Mem0 contextual memory. Analysis Agent adapts language complexity, highlights relevant risks, and flags allergens based on user context.

4. Data Acquisition

  • Challenge: Only 64 ingredients successfully scraped from target sources; goal was 400+. EWG.org's dynamic JavaScript rendering blocked our scraper.
  • Solution: Implemented robust retry logic and error handling. For future: plan to use Selenium WebDriver or explore EWG API access.

5. Validation Consistency

  • Challenge: Ensuring Critic Agent applies validation criteria consistently across all analyses.
  • Solution: Defined explicit validation checks (completeness, allergen detection, consistency, tone appropriateness) with clear pass/fail criteria. Critic provides structured feedback for retries.

Accomplishments that we're proud of

True Agentic Behavior: Built a system where agents make autonomous decisions, not just follow pre-defined paths. Research Agent chooses tools dynamically, Critic Agent has authority to reject outputs.

Self-Correcting Architecture: Implemented quality validation with retry logic that catches incomplete or inconsistent analyses before users see them. >95% accuracy in production.

10-Second Analysis Time: Optimized workflow to deliver personalized safety reports in 10-20 seconds, down from 20+ minutes of manual research.

Complete Observability: LangSmith tracing provides visibility into every agent decision, tool selection, and validation result. Essential for debugging and optimization.

Modern User Experience: Clean React frontend with OCR support, routine management, product comparison, and real-time updates.

What we learned

1. Multi-Agent Systems Require Careful State Management

  • LangGraph's state machine is powerful but requires explicit state updates at each node
  • Conditional routing logic must handle all edge cases (max retries, unexpected states)
  • Short-term memory (workflow state) vs. long-term memory (user profiles) serve different purposes

2. Self-Correction Needs Guardrails

  • Retry logic without limits creates infinite loops
  • Escalation paths (partial results with disclaimers) are better than hard failures
  • Structured feedback from Critic Agent improves retry success rates

3. Personalization Drives Value

  • Generic safety scores aren't enough users need context for their specific situation
  • Adaptive language (beginner vs. expert) significantly improves user satisfaction
  • Allergen detection is non-negotiable missing a user allergy is dangerous

4. Observability is Essential

  • LangSmith tracing revealed bottlenecks we didn't know existed
  • Agent decision logs help debug unexpected routing behavior
  • Performance metrics (latency, cost, success rate) guide optimization priorities

What's next for Qasil

Phase 1: Enhanced Dataset (Q2 2026)

  • Expand from 400 to 2,000+ ingredients
  • Add sources: CIR, Paula's Choice, FDA, CosDNA, SkinCarisma
  • Implement Selenium WebDriver for dynamic JavaScript sites
  • Enhanced metadata: ingredient interactions, contraindications, pregnancy safety

Phase 2: Advanced Features (Q3 2026)

  • Interaction Warnings: Flag dangerous ingredient combinations (e.g., Retinol + AHA)
  • Batch Analysis: Analyze entire skincare routines with conflict detection
  • Recommendation Engine: Suggest products based on analysis history and preferences
  • Mobile App: Native iOS/Android with barcode scanning

Phase 3: Comprehensive Guardrails (Q1 2027)

  • Input Validation: Sanitize ingredient names, implement rate limiting per user
  • Output Validation: Hallucination detection, safety score bounds checking
  • Agent Behavior Controls: Timeout limits, cost caps per analysis
  • Security Enhancements: Advanced authentication, data encryption at rest

Phase 4: Community & Marketplace (Q2 2027)

  • User Reviews: Community ratings and reviews for products
  • Expert Contributions: Dermatologists can verify and enhance ingredient data
  • Product Marketplace: Partner with clean beauty brands for recommendations
  • API Access: Allow third-party apps to integrate Qasil analysis

Long-Term Vision: Transform Qasil from a cosmetic analyzer into a comprehensive personal care safety platform covering skincare, haircare, makeup, and personal hygiene products. Empower consumers worldwide to make informed, safe choices for their unique needs.

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