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Mina

Mina — Your Shopping Concierge takes the stress out of big buying decisions.Tell Mina what you need. She researches, compares, and recommends the best options so you can buy with confidence.

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💡 Inspiration

We built Mina because founders and operators spend too much time making purchasing decisions that don’t move their business forward. Comparing products, checking specs, and verifying vendors is tedious. We wanted an agent that understands your needs, analyzes the market, and gives confident, data-backed recommendations — just like a trusted personal shopper for your business.

⚙️ What It Does

Mina is an intelligent shopping agent that helps businesses find, compare, and buy the right products — fast. Users describe what they need in plain language, and Mina delivers curated comparison cards ranked by price, quality, and fit. She learns user preferences over time, supports team approvals, and can even prepare ready-to-purchase carts.

🛠️ How We Built It

Architecture & Design Philosophy

We built Mina as an AI-powered shopping concierge specifically for high-end purchases ($500+). The architecture follows a modular, integration-first approach with graceful fallbacks.

Core Technologies

  • Python 3.8+ with async/await for concurrent operations
  • Claude Sonnet 4 (Anthropic) for AI-powered product analysis
  • Browser Use for multi-retailer web scraping with anti-bot protection
  • Daytona for secure sandbox code execution (sub-90ms container creation)
  • Galileo SDK for observability, workflow tracing, and confidence metrics
  • Pandas and NumPy for data processing and analysis ## Development Approach
  • Modular Design: Separated concerns into distinct components (scraping, analysis, scoring, presentation)
  • Async-First: Implemented parallel retailer scraping for efficiency
  • Fallback Mechanisms: Every integration works with or without API keys for testing
  • Test-Driven: Built with comprehensive unit and integration tests (6/6 passing)
  • User-Centric: Interactive CLI with color-coded output and progress indicators

Key Workflow Steps

User Input → Browser Scraping → Sandbox Processing → AI Analysis → Confidence Scoring → Recommendations

🧩 Challenges We Ran Into

Balancing simplicity with intelligence — we wanted Mina to feel analytical without overwhelming users. Designing a hierarchy that made complex data easy to digest took iteration. We also had to fine-tune Mina’s tone — trustworthy and warm, but never too chatty.

1. Multi-Integration Orchestration

Challenge: Coordinating Browser Use, Daytona, Claude, and Galileo without creating brittle dependencies Solution: Implemented graceful fallbacks and optional imports - the agent works even without API keys using mock data and rule-based analysis

2. Confidence Score Transparency

Challenge: Making AI recommendations trustworthy and explainable Solution: Developed a multi-factor confidence methodology inspired by Galileo's approach:

  • Customer Ratings (25%)
  • Requirements Fit (40%)
  • Review Confidence (20%)
  • Data Completeness (15%)

🏆 Accomplishments That We’re Proud Of

We created an AI agent that feels genuinely strategic. Mina doesn’t just find products — she reasons, compares, and explains why something is the right fit. We’re also proud of her elegant design language and clear comparison cards that make decision-making effortless.

Challenge: Managing parallel retailer scraping while maintaining data consistency Solution: Used Python's asyncio with proper task management and error handling

4. Secure Code Execution

Challenge: Processing untrusted data safely Solution: Integrated Daytona sandboxes with automatic cleanup and file upload/download capabilities

5. Anti-Bot Detection

Challenge: Major retailers block automated scraping Solution: Leveraged Browser Use's cloud browser mode with anti-bot protection

📚 What We Learned

We learned that great shopping assistance isn’t about automation — it’s about trust. People want clarity, not noise. AI works best when it helps humans think better, not faster. Mina taught us how to balance intelligence, empathy, and design hierarchy in one experience.

🚀 What’s Next for Mina

We’re building vendor integrations so Mina can complete purchases end-to-end, plus adding sustainability scoring and deeper preference learning. Future updates will include team budgeting tools and a style mode that curates products based on aesthetic preferences. Mina will keep evolving into the go-to agent for smart, strategic shopping.

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