https://github.com/kyegomez/whiterock
The world's first fully automated VC fund.
https://github.com/kyegomez/whiterock
ai capital fund llc llp ml vc venture
Last synced: 11 days ago
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The world's first fully automated VC fund.
- Host: GitHub
- URL: https://github.com/kyegomez/whiterock
- Owner: kyegomez
- License: mit
- Created: 2024-06-05T17:08:10.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-04-19T12:54:03.000Z (29 days ago)
- Last Synced: 2025-04-19T20:16:49.828Z (29 days ago)
- Topics: ai, capital, fund, llc, llp, ml, vc, venture
- Language: Python
- Homepage:
- Size: 2.18 MB
- Stars: 21
- Watchers: 1
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
# WhiteRock: The First Fully Autonomous VC Fund
The idea for WhiteRock emerged during a pivotal event that underscored the inefficiencies and limitations of traditional venture capital (VC) operations. Our team observed that while VCs aim to identify and invest in high-potential startups, their decision-making processes are often constrained by human factors, time zones, and the necessity of physical presence. These limitations can hinder their ability to make timely, data-driven investment decisions. This inspired us to create WhiteRock, an autonomous VC fund that leverages advanced AI and operates 24/7 to provide superior returns and revolutionize the VC landscape.
## Install
```bash
$ pip3 install whiterock
```
## ENVs
```
BLAND_API_KEY=""
OPENAI_API_KEY=""
```
## Usage
```python
from whiterock.main import WhiteRock
from whiterock.agents import due_diligence_agent, principal_investor
# Instantiate the WhiteRock class
whiterock = WhiteRock(
agents=[due_diligence_agent, principal_investor],
max_loops=5,
phone_number="+16505188709", ##+19729719060",
phone_call_duration=160,
)
# Run the WhiteRock class
task = "Enter in your task"
whiterock.run(task)
```
## What It Does
WhiteRock is designed to transform the way venture capital operates by automating the entire investment process. Here’s how it works:
1. **Initial Outreach**: The system autonomously reaches out to founders and fundraisers through various channels. It can schedule and conduct interviews using natural language processing (NLP) and conversational AI.
2. **Data Collection**: During these interactions, WhiteRock collects comprehensive data on the startup's product, business model, market traction, financials, and other critical aspects.
3. **Analysis and Evaluation**: The collected data is then passed to the Analyst Agent, which performs a detailed evaluation using machine learning models trained on historical investment data.
4. **Decision Making**: Based on the analysis, the Investor Agent assesses the potential and risks associated with each startup, ultimately making investment recommendations.
5. **Continuous Monitoring**: Post-investment, WhiteRock continuously monitors the performance of portfolio companies, providing insights and recommendations to optimize returns.
## How We Built It
WhiteRock was built using a combination of **Bland.ai API** and our proprietary **Swarms Framework**. Here’s a breakdown of the development process:
### Bland.ai API
- **Conversational AI**: We utilized Bland.ai's state-of-the-art NLP capabilities to enable WhiteRock to autonomously conduct conversations with startup founders. This includes initial outreach, data collection, and follow-up interactions.
- **Data Processing**: Bland.ai's robust data processing tools allowed us to seamlessly integrate and manage large volumes of data collected from various sources.
### Swarms Framework
- **Modular Architecture**: Our Swarms Framework is designed to be highly modular, allowing us to create specialized agents for different tasks within the VC process.
- **Scalability**: The framework is built to scale efficiently, handling an increasing number of startups and investors without compromising performance.
- **Integration**: We integrated various tools and APIs to ensure seamless communication and data flow between agents, enhancing the overall efficiency of the system.
## Challenges We Ran Into
Developing WhiteRock was not without its challenges. One of the biggest hurdles was addressing the human factors inherent in VC decision-making. Traditional VCs rely heavily on intuition, personal connections, and subjective judgment, which are difficult to replicate in an autonomous system. Additionally:
- **Data Sensitivity**: Ensuring the privacy and security of sensitive startup data required implementing robust encryption and access control measures.
- **AI Bias**: Mitigating biases in AI models was crucial to ensure fair and accurate evaluations of startups.
- **Complex Interactions**: Simulating complex, nuanced human interactions with founders required advanced NLP techniques and extensive training data.
## Accomplishments That We're Proud Of
Despite these challenges, we achieved significant milestones, including:
- **First Demo in 5 Minutes**: We successfully demonstrated the core functionality of WhiteRock within just five minutes of launching the prototype. This rapid deployment showcased the system's efficiency and potential impact.
- **24/7 Operation**: WhiteRock’s ability to operate continuously without human intervention is a groundbreaking achievement, ensuring that no investment opportunity is missed due to time constraints.
- **Scalable Architecture**: Our modular and scalable architecture allows WhiteRock to handle an expanding portfolio of startups and investors seamlessly.
## What We Learned
The journey of building WhiteRock provided us with valuable insights, including:
- **Importance of Data Quality**: High-quality data is essential for accurate analysis and decision-making. Ensuring data integrity and reliability was a top priority.
- **Human-AI Collaboration**: While automation can significantly enhance efficiency, human oversight remains important in refining AI models and handling exceptional cases.
- **Continuous Improvement**: The AI models and algorithms powering WhiteRock require continuous updates and improvements to adapt to changing market conditions and emerging technologies.
## What's Next for WhiteRock
As we look to the future, several exciting developments are on the horizon for WhiteRock:
- **Expanding Capabilities**: We plan to enhance WhiteRock’s capabilities by incorporating more advanced AI technologies, such as deep learning and reinforcement learning, to further improve its decision-making processes.
- **Global Reach**: Expanding our reach to global markets will enable us to tap into a broader pool of innovative startups and provide more diverse investment opportunities.
- **Human-AI Synergy**: We aim to foster greater synergy between human investors and AI, leveraging the strengths of both to achieve optimal investment outcomes.
- **Enhanced Monitoring and Support**: Post-investment, we will develop more sophisticated monitoring tools to provide real-time insights and support to portfolio companies, ensuring they achieve their full potential.
- **Community Engagement**: Building a community of founders, investors, and industry experts around WhiteRock to share insights, best practices, and foster collaboration.
## Conclusion
WhiteRock represents a pioneering step in the evolution of venture capital. By leveraging AI and automation, we aim to create a more efficient, scalable, and impactful investment process. Our journey is just beginning, and we are excited to continue pushing the boundaries of what’s possible in the world of venture capital.
# Next steps
- [Tool Integrations: Mercury for bank transcations, api for sending money, api for trackinv investments like carta]