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merged 14 commits into from
Aug 5, 2025
137 changes: 137 additions & 0 deletions docs/showcase/flameguardai.mdx
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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

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Agreed with above; still lots of cutting down that can be done! I would reccommend moving the Claude MCP config section and the Inspiration section to the ReadME of your own github repo. This page should be fairly short (<300 lines) and should only communicate the essentials. @tarunchy

@tarunchy

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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]
---

## πŸ”₯ Inspiration

The devastating LA wildfires were more than a tragic event β€” they were a turning point. While watching homes burn to the ground, I saw a story about one house that miraculously survived. That story stuck with me.

What protected that home? It wasn't luck. It was planning β€” fire-resistant materials, defensible space, and design choices made long before disaster struck. This realization β€” combined with the rising difficulty of securing home insurance β€” inspired us to build **FlameGuard AIβ„’**.

We wanted to create a tool that empowers **homeowners** to take fire risk prevention into their own hands β€” not just react after it's too late.

## 🧠 What it does

**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.

### Demo

[![Watch Live video](https://img.youtube.com/vi/EI5yT7_aD6U/0.jpg)](https://www.youtube.com/watch?v=EI5yT7_aD6U)

### Try it out

- [https://flameguardai.dlyog.com](https://flameguardai.dlyog.com)
- [https://flameguardai-mcp.dlyog.com](https://flameguardai-mcp.dlyog.com)
- [https://github.com/dlyog/fire-risk-assessor-drone-ai](https://github.com/dlyog/fire-risk-assessor-drone-ai)

### Key Features:
- πŸ“Έ Upload a home photo
- πŸ‘οΈ Analyze visible fire risks via **OpenAI Vision API**
- πŸ“š Trigger deep research using the **Perplexity Sonar API**
- πŸ“„ Get a detailed, AI-generated report with:
- Risk summary
- Prevention strategies
- Regional best practices
- πŸ› οΈ Optional contractor referrals for mitigation
- πŸ’¬ 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 β€” **FlameGuard AIβ„’ turns a photo into a plan**.

## βš™οΈ How it works

### The FlameGuard AIβ„’ Process

1. **πŸ“Έ Upload**: User uploads a photo of their property
2. **πŸ‘οΈ AI Vision Analysis**: OpenAI Vision API identifies specific vulnerabilities (e.g., flammable roof, dry brush nearby)
3. **πŸ” Deep Research**: For each risk, we generate a **custom research plan** and run **iterative agentic-style calls** to Perplexity Sonar
4. **πŸ“„ Report Generation**: Research is **aggregated, organized, and formatted** into an actionable HTML report β€” complete with citations, links, and visual guidance
5. **πŸ“§ Delivery**: Detailed report sent via email with DIY solutions and professional recommendations

### πŸ” 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**.

### Technical Stack

FlameGuard AIβ„’ 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

## πŸš€ MCP Integration - FlameGuardAI MCP Server

Transform Claude Desktop into a powerful fire risk assessment tool by connecting it to FlameGuardAI's remote MCP (Model Context Protocol) server. This integration allows you to analyze properties for fire risks, get detailed prevention recommendations, and access professional resources - all through natural conversation with Claude.

### Connect FlameGuardAI to Claude Desktop

Add to your Claude Desktop configuration:

```json
{
"mcpServers": {
"flameguard-ai": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp-flameguardai.dlyog.com/sse"
]
}
}
}
```

With FlameGuardAI MCP integration, Claude can:

- **🏠 Analyze Property Fire Risks**: Describe your property and get comprehensive fire risk assessments
- **πŸ“‹ Generate Prevention Recommendations**: Receive detailed DIY solutions and professional guidance
- **πŸ›’ Find Local Resources**: Get information about material suppliers and professional services in your area
- **πŸ“Š Create Assessment Reports**: Generate detailed reports with actionable fire prevention strategies
- **πŸ” Research Fire Safety**: Access specialized fire prevention research powered by Perplexity AI

## πŸ† 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** β€” not just a demo
- Kept the experience simple, affordable, and scalable from the ground up
- Made structured deep research feel accessible and trustworthy

## πŸ“š What we learned

- 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.

## πŸš€ What's next for FlameGuard AIβ„’ - Prevention is Better Than Cure

We're just getting started.

### Next Steps:
- 🌐 Deploy to **Azure App Services** with production-ready database
- πŸ“± Launch mobile version with location-based scanning
- 🏑 Partner with **home inspection services** and **homeowners associations**
- πŸ’¬ Enhance Claude/MCP integration with voice-activated AI reporting
- πŸ’Έ Introduce B2B plans for real estate firms and home safety consultants
- πŸ›‘οΈ Expand database of **local contractor networks** and regional fire codes

We're proud to stand with homeowners β€” not just to raise awareness, but to enable action.

**FlameGuard AIβ„’ – Because some homes survive when others don't.**

---

**Contact us to know more: [email protected]**