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🧠 What it does

FlameGuard AI™ helps homeowners, buyers, and property professionals detect and act on external fire vulnerabilities like wildfires or neighboring structure fires. It’s more than a scan — it’s a personalized research assistant for your home.

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Demo

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Try it out

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Key Features:

  • 📸 Upload a home photo
  • 👁️ Analyze visible fire risks via OpenAI Vision API
  • 📚 Trigger deep research using the Perplexity Sonar API
  • 📄 Get a detailed, AI-generated report with:
    • Risk summary
    • Prevention strategies
    • Regional best practices
  • 🛠️ Optional contractor referrals for mitigation
  • 💬 Claude (MCP) chatbot integration for conversational analysis
  • 🧾 GDPR-compliant data controls
Whether you’re protecting your home, buying a new one, or just want peace of mind — FlameGuard AI™ turns a photo into a plan.

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⚙️ How it works

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The FlameGuard AI™ Process

  1. 📸 Upload: User uploads a photo of their property
  2. 👁️ AI Vision Analysis: OpenAI Vision API identifies specific vulnerabilities (e.g., flammable roof, dry brush nearby)
  3. 🔍 Deep Research: For each risk, we generate a custom research plan and run iterative agentic-style calls to Perplexity Sonar
  4. 📄 Report Generation: Research is aggregated, organized, and formatted into an actionable HTML report — complete with citations, links, and visual guidance
  5. 📧 Delivery: Detailed report sent via email with DIY solutions and professional recommendations

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🔍 Deep Research with Perplexity Sonar API

The real innovation is how we use the Perplexity Sonar API:
  • We treat it like a research assistant gathering the best available information
  • Each vulnerability triggers multiple queries covering severity, mitigation strategies, and localized insights
  • Results include regional fire codes, weather patterns, and local contractor availability
This kind of structured, trustworthy, AI-powered research would not be possible without Perplexity.

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Technical Stack

FlameGuard AI™ is powered by a modern GenAI stack and built to scale:
  • Frontend: Lightweight HTML dashboard with user account control, photo upload, and report access
  • Backend: Python (Flask) with RESTful APIs
  • Database: PostgreSQL (local) with Azure SQL-ready schema
  • AI Integration: OpenAI Vision API + Perplexity Sonar API
  • Cloud-ready: Built for Azure App Service with Dockerized deployment

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🏆 Accomplishments that we’re proud of

  • Successfully used OpenAI Vision + Perplexity Sonar API together in a meaningful, real-world workflow
  • Built a functioning MCP server that integrates seamlessly with Claude for desktop users
  • Created a product that is genuinely useful for homeowners today — not just a demo
  • Kept the experience simple, affordable, and scalable from the ground up
  • Made structured deep research feel accessible and trustworthy

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📚 What we learned

  • The Perplexity Sonar API is incredibly powerful when used agentically — not just for answers, but for reasoning.
  • Combining multimodal AI (image + research) opens up powerful decision-support tools.
  • Users want actionable insights, not just data — pairing research with guidance makes all the difference.
  • Trust and clarity are key: our design had to communicate complex information simply and helpfully.

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🚀 What’s next for FlameGuard AI™ - Prevention is Better Than Cure

We’re just getting started.

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Next Steps:

  • 🌐 Deploy to Azure App Services with production-ready database
  • 📱 Launch mobile version with location-based scanning
  • 🏡 Partner with home inspection services and homeowners associations
  • 💬 Enhance Claude/MCP integration with voice-activated AI reporting
  • 💸 Introduce B2B plans for real estate firms and home safety consultants
  • 🛡️ Expand database of local contractor networks and regional fire codes
We’re proud to stand with homeowners — not just to raise awareness, but to enable action. FlameGuard AI™ – Because some homes survive when others don’t.
Contact us to know more: info@dlyog.com