🤖 AI Agent Consultant - Complete System

A sophisticated lead generation system that turns user ideas into comprehensive AI agent strategic reports. Built with FastAPI, Next.js, CrewAI, and modern AI tools.

🎯 What This System Does

This is a complete lead generation funnel that:

✅ Captures user's AI agent idea
✅ Has conversational refinement with an AI agent
✅ Generates a free preview (requirements analysis)
✅ Gates full report behind email capture
✅ Generates complete strategic report (4 sections)
✅ Allows 2 free refinements
✅ Scores leads automatically (0-100)
✅ AI agent to write email based generated report
✅ Sends emails via Resend
✅ Notifies sales team of high-value leads

🎨 Features

For Users

Conversational Interface: Natural chat with AI consultant
Free Preview: See quality before providing email
Complete Report: 4 detailed sections covering all aspects
Refinements: Improve report with additional information
Modern UI: Clean, simple, and professional design
Mobile Responsive: Works on all devices

For Business

Lead Scoring: Automatic 0-100 scoring based on engagement and complexity
Email Capture: Two-stage funnel maximizes conversion
Sales Notifications: Real-time alerts for high-quality leads
Analytics Dashboard: Track leads, scores, and conversions
Export Options: Download reports as PDF or JSON or TXT

🏗️ Architecture

Tech Stack

Backend:

FastAPI (REST API)
CrewAI (Multi-agent orchestration)
LangChain + Groq (LLM integration)
MongoDB (Database)
Resend (Email)

Frontend:

Next.js 14 (App Router)
TypeScript
Tailwind CSS
Axios (API client)

Deployment:

Vercel (Frontend)
Render (Backend)
MongoDB Atlas (Database)

📦 Quick Start (5 Minutes)

Prerequisites

Node.js 18+
Python 3.11+
MongoDB (local or Atlas)
API Keys: Groq, Resend

Installation Dependencies

# Clone
cd ai-agent-consultant

# Setup Backend
cd backend
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

# Setup Frontend
cd frontend
npm install

Configuration

Backend .env:

GROQ_API_KEY=gsk_your_key
GROQ_MODEL=mixtral-8x7b-32768
MONGO_URI=mongodb://localhost:27017
MONGO_DB=multiagent_system
Resend_API_KEY=SG.your_key
FROM_EMAIL=noreply@youragency.com
SALES_EMAIL=sales@youragency.com
MAX_TOKEN_EMAIL=2000
MAX_TOKEN_REPORT=2000
PORT=8000

Frontend .env.local:

NEXT_PUBLIC_API_URL=http://localhost:8000
NEXT_PUBLIC_ADMIN_EMAIL=admin@youragency.com
NEXT_PUBLIC_ADMIN_PASSWORD=YourPassword@99!

Run

Terminal 1 - Backend:

cd backend
source venv/bin/activate
python -m uvicorn index:app --reload --log-level debug #Ubuntu
python -m uvicorn index:app --loop asyncio --reload --log-level debug #Windows

# http://localhost:8000

Terminal 2 - Frontend:

cd frontend
npm run dev
# http://localhost:3000

Terminal 3 - MongoDB, If you want to run it locally:

docker run -d -p 27017:27017 --name mongodb mongo:latest

📁 Project Structure

ai-agent-consultant/
└── backend/
│   ├── agents/
│   │   ├── ai_consultant_system.py  # Core multi-agent system
│   │   ├── email_generator.py       # Email Writer agent   
│   ├── index.py                     # FastAPI REST API
│   ├── pdf_generator.py             # PDF generator for report
│   └── requirements.txt             # Python dependencies
│   └── .env.example                 # Environment variables
│
└── frontend/
    └── src/
    │   ├── app/
    │   │   ├── layout.tsx          # Root layout
    │   │   ├── page.tsx            # Landing page
    │   │   ├── conversation/       # Chat interface
    │   │   ├── preview/            # Preview + lead capture
    │   │   └── report/[id]/        # Full report view
    │   ├── components/
    │   │   ├── ui/                 # Reusable UI components
    │   │   ├── SocialProof.tsx
    │   │   ├── ProgressTracker.tsx
    │   │   └── ...
    │   ├── admin/                  # Admin Panel    
    │   │   └── ...
    │   ├── conversation/
    │   │   ├── Page.tsx
    │   ├── lib/
    │   │   ├── types.ts            # TypeScript interfaces
    │   │   ├── api.ts              # API client
    │   │   └── utils.ts            # Helper functions
    │   ├── hooks/
    │   │   ├── useSession.ts
    │   │   ├── useProgress.ts
    │   │   └── useSocialProof.ts
    ├── package.json
    ├── .env.local
    └── next.config.js

🚀 User Flow

1. Landing Page
   ↓ [User enters idea]
   
2. Conversation (Refinement)
   ↓ [AI asks questions]
   ↓ [User answers]
   ↓ [Requirements complete]
   
3. Preview Page
   ↓ [User sees free preview]
   ↓ [User enters email]
   
4. Generating Report (1-2 min)
   ↓ [Shows progress: 0% → 100%]
   ↓ [Displays social proof]
   
5. Full Report
   ↓ [4 tabs: Requirements, Architecture, UX, Business]
   ↓ [User can refine (2x)]
   
6. Sales Conversion
   → [CTA: Book consultation]

🎨 Design Philosophy

Minimal & Modern

Monochromatic color scheme (grays + black)
Generous whitespace
Clean typography
Subtle animations
Mobile-first responsive

User Experience

No overwhelming colors or gradients
Clear visual hierarchy
Fast loading times
Smooth transitions
Accessible design

Example Color Palette

--gray-50: #fafafa;   /* Backgrounds */
--gray-900: #18181b;  /* Text & primary actions */
--white: #ffffff;     /* Cards */
--accent: #3b82f6;    /* Optional accent (minimal use) */

📊 Lead Scoring Algorithm

Leads are scored 0-100 based on:

Engagement (0-25): Number of conversation exchanges
Complexity (0-30): Keywords like API, integration, video, automation
Technical Sophistication (0-20): AI, ML, NLP mentions
Business Clarity (0-15): Target audience, pricing mentions
Detail Level (0-10): Length of requirements

Example Scores:

90+: Enterprise-level, complex AI agent with clear business model
70-89: Well-defined project with technical requirements
50-69: Good idea but needs more refinement
Below 50: Vague or early-stage concept

📧 Email Flow

User Email (Report Delivery)

Email Writer Agent

AI agent takes generated reports + customer idea and below template, to generated unique email for each customer.

Subject: Your AI Agent Strategy Report is Ready, [Name]! 🚀

Hi [Name],

Your complete AI agent strategic report is ready! 🎉

We've analyzed your idea and created a comprehensive blueprint including:
✅ Detailed Requirements Analysis
✅ Complete Technical Architecture
✅ UX Design Framework & User Flows
✅ Business Model & Pricing Strategy

[VIEW FULL REPORT]

Based on our analysis:
• Development Timeline: 8-12 weeks
• Technical Complexity: Medium-High  
• Estimated Investment: $35,000 - $55,000

Want to discuss bringing this to life?
[BOOK FREE CONSULTATION]

Sales Notification

Subject: 🔥 HIGH PRIORITY New Lead: [Name] - $50K Potential

Lead Score: 85/100
Name: John Doe
Email: john@example.com

Project Overview:
AI agent for enterprise video generation with automation...

Estimated Project Value: $50,000

Recommended Action:
🎯 Contact within 24 hours - High quality lead!

[VIEW SESSION DETAILS]

🔧 API Endpoints

Public Endpoints

POST   /conversation/start
POST   /conversation/continue
POST   /preview/generate
POST   /lead/capture
GET    /progress/{session_id}
POST   /report/get
POST   /report/refine
GET    /social-proof

Admin Endpoints

GET    /analytics/overview
GET    /analytics/top-leads
GET    /session/{session_id}/full

Full API documentation: http://localhost:8000/docs

🚀 Deployment to Render (Backend)

Deploying the FastAPI backend to Render.com is simple and fully automated.

1. Create a Render Account

Login at https://render.com → “New → Web Service”.

2. Connect GitHub Repo

Select your repo and choose the backend/ folder.

3. Configuration

Build Command

pip install -r requirements.txt

Start Command

uvicorn api:app --host 0.0.0.0 --port $PORT

4. Environment Variables

GROQ_API_KEY=your_key
GROQ_MODEL=mixtral-8x7b-32768
MONGO_URI=your_mongodb_atlas_uri
MONGO_DB=multiagent_system
RESEND_API_KEY=your_resend_key
FROM_EMAIL=noreply@youragency.com
SALES_EMAIL=sales@youragency.com
MAX_TOKEN_EMAIL=2000
MAX_TOKEN_REPORT=2000
PORT=8000

5. Deploy

Click Deploy Web Service.

🚀 Deployment to Vercel

Deploy frontend

cd ../frontend
vercel

- `NEXT_PUBLIC_API_URL` (your backend URL)

See [Vercel Deployment Guide](./DEPLOYMENT.md) for detailed instructions.

---

## 🔒 Security

- ✅ Environment variables (never in code)
- ✅ Input validation (Pydantic)
- ✅ CORS configuration
- ✅ Rate limiting ready
- ✅ MongoDB connection pooling
- ✅ Error handling
- ❌ No hardcoded secrets
- ❌ No SQL injection (using MongoDB)

---

## 📈 Performance

- **Report Generation**: 1-2 minutes (4 AI agents sequential)
- **Preview Generation**: 20-30 seconds (1 AI agent)
- **Conversation**: 3-5 seconds per message
- **Page Load**: < 1 second (Next.js optimization)

---

## 💰 Cost Breakdown

### Free Tier (Testing)
- Vercel: Free (100GB bandwidth)
- MongoDB Atlas: Free (512MB)
- Groq: Free tier available
- Resend: Free (100 emails/day)

**Total: $0/month**

### Production (Scale)
- Vercel Pro: $20/month
- MongoDB M10: $9/month
- Resend: $15/month (40K emails)
- Groq: Pay-as-you-go

**Total: ~$44/month + usage**

---

## 📚 Documentation

- [API Documentation](http://localhost:8000/docs) - Interactive API docs
- [Deployment Guide](./DEPLOYMENT.md) - Vercel deployment steps
- [Architecture Overview](./ARCHITECTURE.md) - System design
- [Contributing Guide](./CONTRIBUTING.md) - How to contribute

---

## 🐛 Troubleshooting

### MongoDB Connection Error
```bash
# Check if MongoDB is running
docker ps

# Restart MongoDB
docker restart mongodb

Groq API Timeout

# Increase timeout in consultant_system.py
llm = ChatGroq(
    ...,
    request_timeout=120,  # 2 minutes
)

CORS Error

# Update allowed origins in api.py
allow_origins=[
    "http://localhost:3000",
    "https://your-frontend.vercel.app",
]

👥 Contributing

Contributions welcome! Please:

Fork the repository
Create a feature branch
Make your changes
Add tests
Submit a pull request

📄 License

MIT License - feel free to use for commercial projects!

🙏 Acknowledgments

Built with:

📞 Support

📧 Email: hello@cipherslab.com
🐦 Twitter: @ciphersLab

⭐ Star Us!

If this project helped you, please star it on GitHub!

Built with ❤️ for innovators and entrepreneurs

Ready to turn your AI agent idea into reality? Get started now! 🚀

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
backend		backend
frontend		frontend
readme.md		readme.md

CiphersLab/AI-Agent-Consultant

Folders and files

Latest commit

History

Repository files navigation

🤖 AI Agent Consultant - Complete System

🎯 What This System Does

🎨 Features

For Users

For Business

🏗️ Architecture

Tech Stack

📦 Quick Start (5 Minutes)

Prerequisites

Installation Dependencies

Configuration

Run

📁 Project Structure

🚀 User Flow

🎨 Design Philosophy

Minimal & Modern

User Experience

Example Color Palette

📊 Lead Scoring Algorithm

📧 Email Flow

User Email (Report Delivery)

Email Writer Agent

Sales Notification

🔧 API Endpoints

Public Endpoints

Admin Endpoints

🚀 Deployment to Render (Backend)

🚀 Deployment to Render (Backend)

1. Create a Render Account

2. Connect GitHub Repo

3. Configuration

4. Environment Variables

5. Deploy

🚀 Deployment to Vercel

Deploy frontend

Groq API Timeout

CORS Error

👥 Contributing

📄 License

🙏 Acknowledgments

📞 Support

⭐ Star Us!

About

Topics

Resources

Uh oh!

Stars

Watchers

Forks

Releases

Packages 0

Languages

Packages