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AI-Powered VC Startup Matching Platform Enhancement & Scalability

dac.digital

Information technology

Challenges with Platform User-Friendliness, Automation, and AI Integration

DeckMatch experienced difficulties in creating a user-friendly and automated platform while effectively integrating its AI and ML components with the frontend. This hindered efficient matchmaking and scalability. The existing system lacked seamless API integration and required manual processes for key functions.

About the Client

A Norwegian startup providing an AI and ML-powered platform to streamline the process of connecting venture capitalists with promising startups.

Project Goals

Improve the efficiency of the VC-startup matchmaking process.
Enhance platform scalability to accommodate future growth.
Increase user engagement and satisfaction.
Strengthen platform security and data privacy.
Automate machine learning workflows for improved accuracy and reliability.

Functional Requirements

Secure startup and VC profile management.
Automated presentation upload and processing.
AI-powered matching algorithm with configurable filters.
User authentication and authorization (Auth0 integration).
Automated machine learning workflow execution and monitoring.
Data aggregation from external sources (LinkedIn, company websites).
Email notification system (Sendgrid).

Preferred Technologies

FastAPI

PostgreSQL (with SQLAlchemy)

Auth0

Google Cloud Platform (Cloud Storage, Cloud Run, Google Kubernetes Engine)

Kubernetes

Argo Workflows

Selenium (for web scraping)

Sendgrid

Langchain (LLM chat)

Required Integrations

Auth0 (for user authentication)
LinkedIn API (for data aggregation)
Official company website APIs (for data aggregation)
Sendgrid (for email notifications)

Non-Functional Requirements

High scalability to handle increasing user base and data volume.
Robust security measures to protect sensitive user data.
High performance and low latency.
Automated and reliable machine learning workflows.
Intuitive and user-friendly interface.

Expected Business Impact

This project is expected to significantly improve the efficiency of the VC-startup matchmaking process, leading to a 43% increase in matchmaking efficiency. Enhanced scalability, security, and automation will enable DeckMatch to expand its service offerings, solidify its market position, and increase user satisfaction. Improved data aggregation and AI capabilities will further refine the matching process and drive business growth.