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--- | ||
title: "FlameGuardAI" | ||
description: "AI-powered wildfire prevention using OpenAI Vision + Perplexity Sonar API" | ||
sidebar_position: 2 | ||
keywords: [FlameGuardAI, MCP, External Fire ,AI Home Safety, Home Inspection] | ||
--- | ||
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## π§ What it does | ||
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**FlameGuard AIβ’** 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 β it's a personalized research assistant for your home. | ||
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### Demo | ||
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[](https://www.youtube.com/watch?v=EI5yT7_aD6U) | ||
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### Try it out | ||
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- [FlameGuard AI](https://flameguardai.dlyog.com) | ||
- [FlameGuard AI MCP](https://flameguardai-mcp.dlyog.com) | ||
- [GitHub Repo](https://github.com/dlyog/fire-risk-assessor-drone-ai) | ||
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### Key Features: | ||
- πΈ Upload a home photo | ||
- ποΈ Analyze visible fire risks via **OpenAI Vision API** | ||
- π Trigger deep research using the **Perplexity Sonar API** | ||
- π Get a detailed, AI-generated report with: | ||
- Risk summary | ||
- Prevention strategies | ||
- Regional best practices | ||
- π οΈ Optional contractor referrals for mitigation | ||
- π¬ Claude (MCP) chatbot integration for conversational analysis | ||
- π§Ύ GDPR-compliant data controls | ||
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Whether you're protecting your home, buying a new one, or just want peace of mind β **FlameGuard AIβ’ turns a photo into a plan**. | ||
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## βοΈ How it works | ||
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### The FlameGuard AIβ’ Process | ||
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1. **πΈ Upload**: User uploads a photo of their property | ||
2. **ποΈ AI Vision Analysis**: OpenAI Vision API identifies specific vulnerabilities (e.g., flammable roof, dry brush nearby) | ||
3. **π Deep Research**: For each risk, we generate a **custom research plan** and run **iterative agentic-style calls** to Perplexity Sonar | ||
4. **π Report Generation**: Research is **aggregated, organized, and formatted** into an actionable HTML report β complete with citations, links, and visual guidance | ||
5. **π§ Delivery**: Detailed report sent via email with DIY solutions and professional recommendations | ||
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### π Deep Research with Perplexity Sonar API | ||
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The real innovation is how we use the **Perplexity Sonar API**: | ||
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- 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 | ||
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This kind of **structured, trustworthy, AI-powered research would not be possible without Perplexity**. | ||
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### Technical Stack | ||
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FlameGuard AIβ’ is powered by a modern GenAI stack and built to scale: | ||
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- **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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## π Accomplishments that we're proud of | ||
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- 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** β 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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## π What we learned | ||
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- The **Perplexity Sonar API** is incredibly powerful when used agentically β 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 β 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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## π What's next for FlameGuard AIβ’ - Prevention is Better Than Cure | ||
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We're just getting started. | ||
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### Next Steps: | ||
- π Deploy to **Azure App Services** with production-ready database | ||
- π± Launch mobile version with location-based scanning | ||
- π‘ Partner with **home inspection services** and **homeowners associations** | ||
- π¬ Enhance Claude/MCP integration with voice-activated AI reporting | ||
- πΈ Introduce B2B plans for real estate firms and home safety consultants | ||
- π‘οΈ Expand database of **local contractor networks** and regional fire codes | ||
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We're proud to stand with homeowners β not just to raise awareness, but to enable action. | ||
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**FlameGuard AIβ’ β Because some homes survive when others don't.** | ||
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--- | ||
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**Contact us to know more: [email protected]** |
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This file is 550+ lines and tries to do both product storytelling and setup documentation. I'd strongly recommend cutting it down to just being a showcase for your project - what it is, what it does, how it works