🐾 Venture Forge — Multi-Agent Product Launch OS

🎥 Demo Video

_{Click to watch on YouTube}

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✨ What is Venture Forge?

Venture Forge is an intelligent product launch operating system powered by specialized AI agents that work in parallel and sequence across 4 analysis phases to evaluate your product idea end-to-end.

Instead of manual market research that takes weeks, Venture Forge provides a comprehensive analysis in minutes, backed by real-time financial data and competitive intelligence.

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🔄 System Architecture & Flow

The following diagram illustrates how the Master Orchestrator manages the flow of information between specialized agents to reach a final launch decision.

graph TD
    User([User Input]) --> Orchestrator{Master Orchestrator}
    
    subgraph Phase 1: Validation
        Orchestrator --> MA[Market Analyzer]
        Orchestrator --> CS[Customer Specialist]
        Orchestrator --> CI[Competitive Intel]
        MA & CS & CI --> P1[Phase 1 Summary]
    end
    
    subgraph Phase 2: Financial
        P1 --> RA[Revenue Architect]
        P1 --> PS[Pricing Strategist]
        P1 --> RO[Risk Officer]
        RA & PS & RO --> P2[Phase 2 Summary]
    end
    
    subgraph Phase 3: GTM & Product
        P2 --> GS[GTM Strategist]
        P2 --> RL[Roadmap Lead]
        GS & RL --> P3[Phase 3 Summary]
    end
    
    subgraph Phase 4: Decision
        P3 --> LD[Launch Director]
        LD --> Result{GO / NO-GO Decision}
    end
    
    Result --> SL[Self-Learning Loop]
    SL --> DB[(learning_log.json)]
    DB -.-> Orchestrator

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🎯 Key Features

• 🤖 Specialized AI Agents — parallel + sequential orchestration via asyncio.
• 🧬 Self-Learning Loop — agents calibrate credibility via EMA (Exponential Moving Average) after each simulation.
• 📈 Live Financial Data — real-time competitor financials via yFinance (market cap, revenue, stock history).
• 🔍 Google Trends Integration — search interest data feeds into market analysis.
• 🧠 Master Agent Chat — conversational AI assistant with full simulation context.
• 📊 Interactive Dashboard — glassmorphism UI with Chart.js visualizations (market size, revenue projections, risk matrix).
• 🎙️ Modulate (Sponsor) — High-fidelity Speech-to-Text (STT) via the Velma API for voice-driven product brainstorming.
• ⚡ Airia (Sponsor) — Unified AI model orchestration and served inference for a robust multi-agent backbone.
• 🧪 Braintrust (Sponsor) — Enterprise-grade AI evaluation and tracing to ensure agent decision accuracy and performance.

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💡 Dummy Use Case: "EcoEat"

To understand how Venture Forge works, let's follow a dummy product idea through the system:

Product Idea: An AI-powered meal planning app that minimizes food waste by suggesting recipes based on items already in the user's fridge.

The Flow:

1. Input: User enters "EcoEat", target audience "Environmentally conscious urban professionals", and business model "Premium Subscription".
2. Validation Phase:
   • Market Analyzer: Identifies a 15% CAGR in the sustainable food-tech sector.
   • Customer Specialist: Maps out "Busy Brenda" who hates throwing away expensive organic produce.
3. Financial Phase:
   • Revenue Architect: Projects $1.2M ARR by year 2 based on a 3% conversion rate.
   • Risk Officer: Flags "High Competition" from established players like HelloFresh.
4. Decision Phase:
   • Launch Director: Issues a GO decision with a 78% Confidence Score, suggesting a "B2B partnership with grocery chains" to mitigate competition risk.
5. Learning Loop: After the simulation, the system "simulates" market performance, adjusting the Risk Officer's credibility score because it accurately predicted the competitive hurdle.

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🛠️ Tech Stack

Layer	Technology
Backend	Python 3.12, Flask, Flask-CORS
AI/LLM	Groq API, Airia (Orchestration), Braintrust (Eval)
Data APIs	yFinance, Google Trends, Modulate (STT)
Frontend	Vanilla JS, HTML5, CSS3, Chart.js

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🚀 Quick Start

Prerequisites

• Python 3.9+
• UV (recommended)

Installation

# 1. Clone
git clone https://github.com/Krishhhhh05/Datadogs.git
cd Datadogs

# 2. Install dependencies
uv sync            # or: pip install -r requirements.txt

# 3. Set your API key in .env
echo "GROQ_API_KEY=your_key_here" > .env

# 4. Start the backend
uv run python backend/app.py

Open frontend/app.html in your browser to start.

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🏗️ Project Structure

Datadogs/
├── backend/
│   ├── app.py                    # Flask API
│   ├── core/                     # Orchestration & Services
│   ├── agents/                   # The 9 Agent Definitions
│   └── data/                     # Persisted Learning Logs
├── frontend/                     # UI components (app.js, styles.css)
├── main.py                       # CLI version
└── run.sh                        # Startup script

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📝 License

MIT License.

Made with ❤️ by Venture Forge

Special thanks to our sponsors: Airia, Braintrust, and Modulate