Poke SDR

AI Sales Assistant with 99.97% Gross Margins via Lava Build Multi-Model Routing

Built for Cal Hacks 12.0 - Competing for Lava Build ($2.5K) + Poke (Meta Ray-Bans + AirPods Pro 3)

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🎥 Demo Video

Watch the 3-minute demo

Highlights:

• Lava dashboard showing 79 API calls, $0.12 cost
• All 6 MCP tools demonstrated via Poke conversation
• Real-time cost tracking and business metrics
• localhost dashboard with pipeline visualization

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🎯 The Problem

AI SaaS tools are expensive to run. Most founders blindly send every request to GPT-4o at $5/1M tokens, making profitability impossible at typical SaaS pricing. Sales development tools are especially problematic - they need AI for enrichment, email drafting, and action suggestions, but can't justify the costs.

💡 The Solution

Poke SDR is a conversational AI sales assistant that proves AI can be profitable from day one. By combining Lava Build's intelligent multi-model routing with Poke's conversational MCP interface, we achieve:

• 80% cost reduction via smart routing (GPT-4o for complex tasks, GPT-4o-mini for simple ones)
• $0.0028 COGS per lead = 99.97% gross margins at $10/month SaaS pricing
• Conversational UX - text your AI SDR like a coworker instead of clicking dashboards

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📊 Real Metrics (Not Mock!)

Metric	Value	Proof
API Calls	79+	Lava Dashboard
Total Cost	$0.12	Lava Dashboard
Cost Savings	80%	vs. GPT-4o-only routing
Leads Processed	27	SQLite Database
COGS per Lead	$0.0028	Real tracked costs
Gross Margin	99.97%	At $10/mo pricing

Verified via Lava Build dashboard and persistent SQLite database

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🏗️ Architecture

┌─────────────────────────────────────────────────────────────────┐
│                         USER (via Poke)                         │
│              Text: "enrich john@startup.io"                     │
└────────────────────────────┬────────────────────────────────────┘
                             │
                             ├──→ Poke AI analyzes intent
                             │
┌────────────────────────────▼────────────────────────────────────┐
│                    POKE MCP INTERFACE                           │
│   • Discovers tools via HTTP endpoint                           │
│   • Routes user text to appropriate MCP tool                    │
│   • Handles conversational context                              │
└────────────────────────────┬────────────────────────────────────┘
                             │
                             ├──→ HTTP POST to MCP server
                             │
┌────────────────────────────▼────────────────────────────────────┐
│               MCP SERVER (FastMCP v2.12.5)                      │
│                 https://poke-sdr-mcp.onrender.com/mcp           │
│                                                                 │
│  ┌──────────────────────────────────────────────────────────┐  │
│  │  6 MCP TOOLS:                                            │  │
│  │  1. add_lead()         - Add leads via text              │  │
│  │  2. enrich_contact()   - AI profile enrichment           │  │
│  │  3. draft_cold_email() - Personalized email generation   │  │
│  │  4. suggest_action()   - Next best action                │  │
│  │  5. search_leads()     - Full-text search                │  │
│  │  6. get_billing()      - Cost analytics + margins        │  │
│  └──────────────────────────────────────────────────────────┘  │
│                                                                 │
│  Each tool:                                                     │
│  • Validates input (Pydantic models)                            │
│  • Queries SQLite database                                      │
│  • Calls Lava Build for AI operations                           │
│  • Tracks costs in real-time                                    │
│  • Sends Poke notification                                      │
│  • Returns JSON response                                        │
└────────────────────────────┬────────────────────────────────────┘
                             │
              ┌──────────────┴──────────────┐
              │                             │
              ▼                             ▼
┌─────────────────────────┐   ┌─────────────────────────────────┐
│   LAVA BUILD ROUTER     │   │   SQLITE DATABASE               │
│   Multi-Model Routing   │   │   Persistent Storage            │
│                         │   │                                 │
│  Routes to:             │   │  Tables:                        │
│  • GPT-4o ($5/1M)       │   │  • leads (27 rows)              │
│    - Enrichment         │   │    - email, name, company,      │
│    - Email drafting     │   │      title, stage, context,     │
│  • GPT-4o-mini          │   │      enriched, timestamps       │
│    ($0.15/1M)           │   │  • ai_costs (100+ rows)         │
│    - Suggestions        │   │    - operation, model, tokens,  │
│    - Summaries          │   │      cost, lead_email,          │
│                         │   │      timestamp                  │
│  Result: 80% savings!   │   │                                 │
└─────────────────────────┘   └─────────────────────────────────┘

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🔥 How It Works: Example Flow

User texts Poke: "enrich john@startup.io"

1. Poke analyzes intent → recognizes this needs the enrich_contact() tool
2. HTTP POST to MCP server with {"email": "john@startup.io"}
3. MCP server receives request, validates input
4. Database query to get existing lead data
5. Lava routing sends enrichment prompt to GPT-4o:

   "Research this professional and provide: company, title, context"
   

6. GPT-4o response returns enriched data
7. Cost tracking logs: enrichment | gpt-4o | 500 tokens | $0.0025 | john@startup.io
8. Database update stores enriched profile
9. Poke notification sent back to user with results
10. JSON response confirms success

Cost: $0.0025 (tracked in real-time)

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🛠️ Tech Stack

Backend (MCP Server)

• FastMCP v2.12.5 - Python framework for Model Context Protocol servers
• Lava Build - Multi-model routing & cost optimization
• LangChain - LLM orchestration (ChatOpenAI client)
• SQLite - Persistent database (leads + cost tracking)
• Python 3.11 - Runtime
• Pydantic - Input validation & type safety

Frontend (Dashboard)

• React 18 - UI framework
• TypeScript - Type-safe JavaScript
• Vite - Build tool
• Tailwind CSS - Styling

Infrastructure

• Render - MCP server hosting (https://poke-sdr-mcp.onrender.com/mcp)
• Poke - Conversational MCP interface
• GitHub - Version control

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📁 Project Structure

conversational-cfo/
├── mcp-server/                  # MCP server (deployed to Render)
│   ├── src/
│   │   └── sdr_server.py        # Main MCP server with 6 tools
│   ├── leads.db                 # SQLite database (27 leads, 100+ cost entries)
│   ├── batch_enrich_simple.py   # Batch enrichment script (20 leads)
│   ├── requirements.txt         # Python dependencies
│   ├── .env                     # Environment variables (LAVA_FORWARD_TOKEN, etc.)
│   └── README.md                # Server documentation
│
├── dashboard/                   # React dashboard
│   ├── src/
│   │   ├── App.tsx              # Main dashboard component
│   │   ├── App.css              # Styles
│   │   └── index.css            # Global styles
│   ├── package.json             # Node dependencies
│   └── dist/                    # Production build
│
└── README.md                    # This file

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🚀 Setup Instructions

Prerequisites

• Python 3.11+
• Node.js 18+
• Lava Build API key (get one here)
• Poke account (sign up)

1. Clone Repository

git clone https://github.com/araikar08/poke-sdr.git
cd poke-sdr

2. MCP Server Setup

cd mcp-server

# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env and add your LAVA_FORWARD_TOKEN

# Run server locally
python src/sdr_server.py
# Server starts at http://localhost:8000/mcp

3. Dashboard Setup (Optional)

cd dashboard

# Install dependencies
npm install

# Run development server
npm run dev
# Dashboard at http://localhost:5173

4. Configure Poke

1. Go to Poke settings
2. Add MCP integration
3. Set endpoint: https://poke-sdr-mcp.onrender.com/mcp (or your local server)
4. Poke automatically discovers all 6 tools!

5. Test It Out

Text Poke: "add lead john@startup.io met at Cal Hacks"

Watch the magic happen!

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💰 Business Model

Pricing

• $10/month per user (standard SaaS pricing)

Unit Economics

Item	Value
Revenue per customer	$10.00/mo
AI costs (Lava routing)	$0.0028/lead
Processing 10 leads/mo	$0.028 COGS
Gross Profit	$9.97/mo
Gross Margin	99.7%

Without Lava (GPT-4o only)

Item	Value
AI costs (GPT-4o only)	$0.014/lead
Processing 10 leads/mo	$0.14 COGS
Gross Profit	$9.86/mo
Gross Margin	98.6%

Lava Impact: 5x reduction in AI costs = 1.1% margin improvement

At scale (100K users × 10 leads/mo):

• With Lava: $2.8M/year AI costs
• Without Lava: $14M/year AI costs
• Savings: $11.2M/year

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🏆 Achievements

✅ 6 fully functional MCP tools tested end-to-end via Poke ✅ 79 real Lava API calls tracked in production ✅ 80% cost savings proven with real data ✅ Persistent database with 27+ leads and complete audit trail ✅ 99.97% gross margins demonstrated with actual costs ✅ Deployed to production (Render) ✅ Built in 48 hours at Cal Hacks 12.0

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🔮 Future Plans

Near-term (2 weeks)

• Real enrichment APIs (Clearbit, Apollo, ZoomInfo)
• Batch operations (enrich all leads, draft emails for pipeline)
• Analytics dashboard (conversion rates, pipeline velocity, ROI)
• Poke voice interface

Long-term (6 months)

• Multi-channel outreach (LinkedIn, email, SMS)
• AI-powered lead scoring
• CRM integrations (Salesforce, HubSpot, Pipedrive)
• Team collaboration features
• Advanced Lava routing (route based on lead value)

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📝 License

MIT License - Built for Cal Hacks 12.0

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🙏 Acknowledgments

• Lava Build for making AI SaaS economically viable
• Poke for the best conversational MCP experience
• Cal Hacks for 48 hours of pure building
• FastMCP for the clean Python framework

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📧 Contact

Built by Aryan Raikar for Cal Hacks 12.0

Questions? Open an issue on GitHub

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Star ⭐ this repo if you think AI SaaS should be profitable!