-
Notifications
You must be signed in to change notification settings - Fork 1
Open Questions & Project Risks
This page documents the key risks and open questions that the project team will need to address. Acknowledging these challenges upfront is crucial for successful project management and mitigation planning.
-
Data Quality and Availability: The effectiveness of our AI agents is heavily dependent on the quality, accuracy, and accessibility of the input data provided for target companies. Incomplete or poor-quality data will lead to less reliable analysis.
-
LLM Reliability and Cost: The performance of Large Language Models (LLMs) can be inconsistent. We will need to manage the "hallucinations," accuracy, and consistency of LLM responses for various agent tasks. Furthermore, the cost of frequent API calls to powerful models needs to be monitored and optimized.
-
Complexity of Agent Orchestration: Designing, debugging, and managing the complex interactions between multiple AI agents can be challenging. Ensuring seamless data flow and error handling between agents is a significant technical hurdle.
-
Defining "Good" Due Diligence: The standard for high-quality due diligence is often subjective and based on the experience of seasoned VCs. Quantifying this standard and training our AI agents to achieve a comparable level of insight will be a continuous process.
-
Scalability: The performance of our Celery and agent microservice architecture will need to be tested under heavy load to identify and resolve potential bottlenecks.
-
Data Security: The platform will handle highly sensitive and confidential startup data. Implementing robust security measures to protect this data from unauthorized access is of the utmost importance.